— vas (@vasuman) January 11, 2026
Really? This is groundbreaking.
— Paramendra Kumar Bhagat (@paramendra) January 23, 2026
The Technologies Behind Agentic AI https://t.co/O6CkqjDXOQ
— Paramendra Kumar Bhagat (@paramendra) January 23, 2026
— Paramendra Kumar Bhagat (@paramendra) January 23, 2026
100x a business with ai AI Agents are not magic, but also are not as simple as "build an agent, automate everything, profit". Most people don’t understand what an agent is. ......... Those that do (Bad context management is an agent that calls the same tool repeatedly because it forgot it already got the answer, or calls the wrong tool because it was fed the wrong information. Another example is an agent that makes a decision contradictory to something it learned two steps earlier, or an agent that treats every task as brand new even when there's a clear pattern from previous similar tasks. .................
Good context management means the agent operates like someone with domain knowledge.
............. The right way is: "This will let three people do what used to require fifteen." .............. The agent did all of the work, but the human approves it. The reality of the situation, from what I’ve seen doing this for customers, is they never fire employees. ............ There’s nearly infinite work for employees to do in place of their previous manual work, at least for now. ........... The companies getting real value from agents aren't the ones trying to remove humans from the loop. Instead they are the ones who realized that most of what humans were doing wasn't actually the valuable part of their job, but rather the overhead required to get to the valuable part. .............. If you get this wrong, you'll spend months debugging failures that aren't even bugs; they're architectural mismatches between your design, your problem, and your solution. ............ When a supplier misses a milestone, don't wait for someone to notice. Trigger the contingency playbook. Start the response before anyone has to manually realize there's a problem. ............Your AI Agent’s job is to make problems impossible to ignore and incredibly easy to resolve.
............. AI SaaS is going nowhere. And the industry is confirming this stat: most companies purchasing AI SaaS churn within 6 months, and see absolutely no productivity gains from implementing AI. The only companies who see AI gains are those who have custom agents built specifically for them, either in house or by a 3rd party agency. ............. This is why the companies that figure out agents early will have a structural advantage for years. They're building infrastructure that gets better over time. Everyone else is renting tools that will eventually need to be replaced. And when the space is changing every month, every week lost has serious implications for your roadmap and your business as a whole. ............. Context is the whole game. An agent without good context is just an expensive random number generator. Invest in how information flows, how memory persists, how domain knowledge gets embedded. Remember when you guys made fun of prompt engineers? Context engineers are just prompt engineers 2.0.
From Demo Candy to Production Steel: How to Build AI Agents That Actually Work in the Enterprise
Lessons from Vasuman, CEO of Varick AI (January 11, 2026)
In the rapidly inflating universe of AI agents, glossy demos are everywhere. Agents book meetings, write emails, summarize documents, and occasionally dazzle investors in carefully choreographed environments. Yet when released into the wild—inside real enterprises with legacy systems, contradictory data, human unpredictability, and compliance landmines—many of these agents collapse like paper bridges in a monsoon.
Vasuman, CEO of Varick AI, calls this out bluntly in his January 11, 2026 article. His piece is not hype, prophecy, or futurism. It is a field manual—written by someone who has built, deployed, and debugged agentic systems in production. The article is ultimately about one thing: how to design AI agents that survive contact with reality and deliver sustained business value.
Below is a synthesized, expanded interpretation of his core ideas, with added context and perspective for enterprise leaders and builders navigating the agentic era.
The Core Problem: Why AI Agents Win Demos and Lose Reality
The article opens with a hard truth: most AI agents fail not because models are weak, but because architectures are naïve.
A common mistake is treating agents like deterministic APIs—clean inputs in, clean outputs out. That mental model works for software functions, not for enterprises. Real workflows are messy. Data is incomplete. Humans interrupt processes. Systems contradict each other. Policies change mid-stream.
Agents designed with rigid assumptions behave like brittle glass sculptures: impressive on display, useless under stress.
Enterprise reality demands adaptability, not perfection.
Adaptability Over Over-Engineering: Designing for Chaos
One of the article’s most counterintuitive points is its warning against over-engineering.
Many teams attempt to anticipate every edge case upfront. The result? Endless design cycles, delayed launches, and agents that still fail when confronted with novel situations.
Vasuman argues instead for adaptive architectures—systems that can reason through ambiguity, ask for help, and adjust dynamically. Launch quickly with a robust core, observe real-world behavior, and iterate based on actual failure modes rather than imagined ones.
Think of it less like building a watch and more like training a firefighter: you prepare for common scenarios and ensure resilience under pressure, not script every possible disaster.
The 80/20 Rule of Automation: Reliability Beats Total Autonomy
A central pillar of the article is the 80/20 automation rule.
Trying to automate 100% of workflows is a trap. The last 10–20% of cases are usually the most complex, rare, and risky—and chasing them often destroys ROI.
Instead, Vasuman advocates:
Automate the 80–90% of cases that occur most frequently
Handle them reliably and repeatedly
Route the remaining edge cases to humans, with full context preserved
This human-in-the-loop approach isn’t a failure of AI—it’s a recognition of reality. Done right, humans become exception handlers, not bottlenecks. Resolution becomes faster, safer, and auditable.
In practice, this is how enterprises win: machines handle the volume, humans handle the judgment.
Memory Is Not a Feature—It Is the Spine
Perhaps the most critical technical insight in the article is the emphasis on memory as foundational architecture, not an add-on.
Many agents fail because they forget:
Prior decisions
Earlier constraints
Why something was done in the first place
An agent that cannot remember step 3 when it reaches step 10 will inevitably make inconsistent or even dangerous decisions.
Vasuman stresses building memory into the system from day one:
Structured, queryable memory
Integration with existing databases and systems of record
Long-term retention, not just session context
In metaphorical terms: an agent without memory is like a consultant with amnesia—confident, articulate, and disastrously unreliable.
Custom Beats Generic: Why SaaS Agents Plateau at 60%
The article draws a sharp distinction between generic AI SaaS tools and custom-built enterprise agents.
Off-the-shelf solutions can be useful for surface-level tasks, but they typically cap out at covering ~60% of workflows. Beyond that point, integration friction, data silos, and contextual blind spots erode value.
High-impact agents, Vasuman argues, must be:
Trained on the company’s proprietary knowledge
Deeply integrated into existing ERP, CRM, finance, and ops systems
Designed around actual workflows, not generic abstractions
In other words, enterprise agents are infrastructure, not apps. Treating them otherwise guarantees diminishing returns.
Implementation Wisdom: Start Small, Ship Fast, Learn Ruthlessly
The article repeatedly returns to execution discipline:
Start with one narrow, high-impact workflow
Ship early
Observe failures
Iterate based on real user behavior
Polished demos are irrelevant if systems crumble under load. What matters is whether agents can function in chaotic, adversarial environments—exactly where enterprises live.
A mediocre model wrapped in strong architecture will outperform a cutting-edge model strapped to a weak system every time.
Where Agents Deliver Immediate ROI
Drawing from Varick AI’s real-world deployments, the article highlights sales enablement and finance/accounting as particularly fertile ground:
Sales agents that surface context, prep calls, and manage follow-ups
Finance agents that reconcile data, flag anomalies, and accelerate close cycles
These domains combine high repetition, clear economic impact, and structured data—ideal conditions for agentic leverage.
Agents as Multipliers, Not Replacements
One of the most resonant metaphors in the piece is the idea of “agents to multiply.”
The goal is not to replace humans, but to amplify them—turning individuals into force multipliers by removing cognitive load, administrative drag, and repetitive decision-making.
This framing matters culturally. Enterprises that treat agents as collaborators move faster, face less resistance, and extract more value than those that frame AI as a zero-sum replacement game.
Looking Ahead: Security, Guardrails, and the Hard Problems
The article closes by promising a Part 2, focused on advanced topics like:
Security
Guardrails
Governance for complex, multi-agent systems
This is a natural progression. As agents move from assistants to actors—capable of taking actions across systems—the cost of mistakes rises dramatically. Architecture, again, will matter more than models.
Final Takeaway
Vasuman’s article stands out because it speaks the language of production, not prediction.
Its core message is simple but profound:
Enterprise AI success is not about smarter models—it’s about resilient architecture, pragmatic automation, and respect for real-world complexity.
In a market flooded with demos and declarations, this kind of grounded thinking is not just refreshing—it is necessary.
Why AI Agent Security Must Come First (Not After the Demo)
If you don’t design for failure, your AI will eventually fail you
In the early days of AI agents, security was treated like seasoning—something you sprinkle on at the end, once the dish looks good enough to serve. Build the agent. Ship the demo. Impress stakeholders. Then, maybe, add guardrails.
That mindset is already obsolete.
As AI agents move from passive copilots to active systems that read, decide, and act inside enterprises, security is no longer a layer. It is the foundation. An unsecured agent is not just buggy software—it is an employee with unlimited access, no judgment, and no sense of consequence.
In other words: an insider threat you built yourself.
From Chatbots to Actors: Why the Risk Profile Has Changed
Traditional software fails quietly. An AI agent fails loudly—and at scale.
Modern agents:
Read emails, tickets, contracts, and logs
Write to CRMs, ERPs, ledgers, and internal tools
Trigger workflows, approvals, refunds, deployments
Coordinate with other agents autonomously
This shift—from responding to acting—fundamentally changes the threat model.
If a chatbot hallucinates, it embarrasses you.
If an agent hallucinates, it can:
Approve the wrong payment
Expose sensitive data
Violate compliance rules
Cascade errors across systems
Security is no longer about protecting the AI.
It’s about protecting the business from the AI.
The Three New Attack Surfaces No One Talks About
Most teams still secure agents as if they were APIs or SaaS apps. That’s a category error. Agentic systems introduce entirely new vulnerabilities.
1. Context Poisoning
Agents reason based on context: memory, documents, logs, and prior actions. If that context is corrupted—intentionally or accidentally—the agent’s decisions degrade silently.
A poisoned memory is worse than bad data. It becomes bad judgment.
2. Tool Misuse and Escalation
Agents don’t just “think.” They call tools.
Every tool call is a potential escalation of privilege:
Who authorized this action?
Under what assumptions?
With what blast radius?
Without strict scoping, agents become overpowered interns with admin access.
3. Agent-to-Agent Contagion
In multi-agent systems, failure spreads.
One compromised or misaligned agent can:
Feed incorrect state to others
Trigger cascaded actions
Create feedback loops that look “reasonable” but are catastrophically wrong
This is not a bug. It’s emergent behavior.
Guardrails Are Not Enough (And Never Were)
Most vendors respond to these risks with the same answer: guardrails.
Guardrails help. They are necessary.
But guardrails alone are like seatbelts on a car with no brakes.
Real agent security requires architectural constraints, not just policy prompts.
That means:
Hard permissions, not soft instructions
Explicit action scopes, not implied trust
Auditable state transitions, not black-box reasoning
Security must be designed, not retrofitted.
A Better Mental Model: Agents as Junior Employees
Here’s a more useful way to think about agent security:
Treat every AI agent like a junior employee on their first day.
You would never:
Give them access to every system
Let them approve transactions unsupervised
Trust them without logs, reviews, or escalation paths
Yet teams routinely do this with AI agents—because they confuse fluency with competence.
Good security design asks:
What should this agent never be allowed to do?
When must a human approve?
What happens when the agent is uncertain?
How do we shut it down instantly if something goes wrong?
These are organizational questions, not model questions.
Human-in-the-Loop Is a Security Feature, Not a Weakness
One of the most dangerous myths in AI is that autonomy equals maturity.
In reality, managed autonomy wins.
The most secure enterprise agents:
Handle the predictable 80–90% autonomously
Escalate the rest with full context
Log every decision and action
Defer judgment when confidence drops
Humans are not bottlenecks.
They are circuit breakers.
Security-conscious systems don’t eliminate humans—they position them where it matters most.
Why Enterprises That Lead With Security Move Faster
Ironically, teams that prioritize security early ship faster in the long run.
Why?
Fewer production rollbacks
Less stakeholder panic
Higher trust from compliance and legal
Faster expansion into new workflows
Security-first agents scale horizontally without fear. Security-last agents stall the moment they touch real money, real data, or real customers.
Speed without control is not velocity. It’s drift.
The Coming Reckoning: Regulation, Liability, and Blame
By 2026, one thing is clear: when an AI agent causes harm, “the model did it” will not be an acceptable answer.
Regulators, courts, and boards will ask:
Who designed the system?
Who approved its permissions?
What safeguards existed?
Why wasn’t a human involved?
The organizations that survive this shift will be the ones that treated agent security as governance infrastructure, not technical hygiene.
Final Thought: Build Like You Expect Failure
Every complex system fails eventually. AI agents are no exception.
The question is not if something goes wrong.
It’s whether you designed the system assuming it would.
Security-first agent design is not pessimism.
It’s professionalism.
Because in the enterprise, the most dangerous AI is not the one that’s malicious—it’s the one that’s confidently wrong.
Guardrails, Not Gates: How to Design Safe AI Workflows That Scale With Complexity
Why the future of enterprise AI isn’t about stopping agents—it’s about steering them
The first instinct when introducing AI agents into serious enterprise systems is fear. And fear produces gates.
Approval gates. Permission gates. Hard stops everywhere.
Nothing moves unless a human signs off.
At first, this feels responsible. Safe. Mature.
But over time, it creates a different failure mode: paralysis.
The organizations that succeed with AI agents don’t trap them behind walls. They guide them with guardrails—structures that allow motion while preventing catastrophe. The difference matters. A lot.
Gates Stop Work. Guardrails Shape It.
A gate is binary. Yes or no. Pass or fail.
A guardrail is directional. It assumes movement and keeps it within bounds.
Enterprise AI systems are not static workflows. They are living systems operating in messy environments: partial data, ambiguous requests, edge cases, and constant change. Designing them like traditional approval pipelines breaks the very thing that makes agents valuable.
The goal is not to prevent agents from acting.
The goal is to make the wrong actions impossible and the right ones inevitable.
That requires architectural thinking, not policy theater.
Why Traditional Controls Collapse Under Agentic Systems
Most enterprises try to secure AI agents using the same playbook they use for SaaS tools:
Role-based access
Static permissions
Manual approvals
Compliance checklists
These tools work for deterministic software. They fail for probabilistic systems.
AI agents:
Reason in steps
Make trade-offs
Operate across tools
Adapt based on context
When you wrap that in rigid gates, you get one of two outcomes:
The agent becomes useless and slow
Teams quietly bypass controls to “get work done”
Neither is safe.
The Guardrail Stack: How Safe Agents Are Actually Built
High-performing agentic systems rely on layers of guardrails, each designed to constrain a different type of risk.
1. Intent Guardrails
Before execution, the agent must articulate why it is acting.
This isn’t for explainability theater. It’s for validation.
If the intent doesn’t align with:
Business goals
Policy constraints
User authorization
The action never happens.
Intent clarity catches more failures than output filtering ever will.
2. Action Scope Guardrails
Every agent action must be scoped:
What system?
What object?
What permissions?
What maximum impact?
A refund agent shouldn’t also update contracts.
A reporting agent shouldn’t trigger payments.
If an agent can’t physically do the wrong thing, it won’t.
3. Confidence-Based Escalation
Not all decisions deserve the same autonomy.
Mature systems monitor:
Model confidence
Context completeness
Novelty of the situation
As uncertainty rises, autonomy drops.
This is not weakness. It’s adaptive intelligence.
4. Rate and Blast-Radius Limits
Most disasters aren’t caused by one bad action. They’re caused by many bad actions executed quickly.
Guardrails throttle:
Execution frequency
Volume of changes
Cross-system propagation
The system assumes errors will happen—and contains them.
5. Observability as a First-Class Primitive
If you can’t see what an agent is doing, you don’t have control—you have hope.
Effective guardrails require:
Action logs
State snapshots
Decision traces
Replay capability
Security without observability is just optimism in a suit.
Why Guardrails Scale Better Than Gates
As workflows grow more complex, gates multiply.
And multiplied gates create exponential friction.
Guardrails, by contrast:
Generalize across workflows
Adapt to new tools
Enable safe parallelism
Reduce human bottlenecks
They scale horizontally, not vertically.
This is why the most advanced enterprises are moving away from “approval-driven AI” and toward constraint-driven autonomy.
The Hidden Benefit: Better Agent Performance
Here’s the counterintuitive truth:
Agents perform better when constrained.
Clear boundaries:
Reduce hallucinations
Improve planning
Prevent overreach
Increase reliability
Ambiguity doesn’t make agents creative. It makes them sloppy.
A well-guardrailed agent is calmer, more focused, and more useful—because it knows the shape of the world it’s allowed to operate in.
Designing for Chaos, Not Perfection
Enterprise environments are not clean. Data is missing. Humans contradict themselves. Systems are half-migrated. Policies evolve.
Guardrails acknowledge this reality.
They don’t assume perfect inputs.
They assume entropy—and design resilience.
That mindset shift is the difference between agents that survive production and agents that quietly get turned off after a few scary incidents.
Final Thought: Autonomy Is a Spectrum, Not a Switch
The biggest mistake teams make is treating autonomy like a toggle: on or off.
In reality, autonomy is a dial.
Guardrails let you tune that dial intelligently—by workflow, by risk, by context.
The future of enterprise AI isn’t fully autonomous agents roaming free.
It’s well-governed systems that know when to move fast, when to slow down, and when to ask for help.
That’s not less powerful.
That’s how power lasts.
The Architecture Beats the Model: Why “Good Enough” AI Wins in the Real World
Or, why a mediocre brain inside a great body outperforms a genius trapped in a glass box
In every AI hype cycle, there’s a familiar ritual.
A new model drops. Benchmarks explode. Twitter lights up.
Executives ask the same question: “Should we switch to this?”
Most teams answer yes.
Most teams are wrong.
In real enterprise environments, architecture matters more than model quality—often by an order of magnitude. A well-architected system built on a “good enough” model will consistently outperform a fragile system powered by the latest, most expensive intelligence.
This isn’t a philosophical stance. It’s a production lesson learned the hard way.
The Myth of the Supermodel
Modern LLMs are astonishing. But they are not magic. They are components.
Treating the model as the product leads to predictable failure modes:
Context loss
Inconsistent reasoning
Unbounded actions
Silent errors
Fragile edge cases
When something breaks, teams reflexively upgrade the model.
That’s like fixing a collapsing bridge by replacing the asphalt.
Architecture Is Where Intelligence Actually Lives
In production systems, intelligence emerges from how components interact, not from raw model capability.
Strong architectures provide:
Memory continuity
Tool discipline
State awareness
Error recovery
Human escalation paths
Weak architectures turn even the smartest model into a liability.
A great model with no memory is goldfish-smart.
A great model with no constraints is dangerous.
A great model with no observability is untrustworthy.
Why Enterprises Keep Overweighting Models
There are three reasons this mistake persists:
1. Models Are Easy to Compare
Benchmarks give the illusion of objectivity. Architectures don’t.
2. Vendors Sell Models, Not Systems
Model improvements are marketable. Robust workflows are boring.
3. Demos Reward Intelligence, Not Reliability
A demo only has to work once. Production has to work every day.
As a result, enterprises optimize for the wrong thing—and pay for it later.
The “80/20 Architecture Rule”
High-performing teams internalize a simple truth:
A stable architecture that handles 80–90% of cases reliably is more valuable than a perfect model that fails unpredictably.
Why?
The last 10% is where chaos lives
Humans are better suited for true edge cases
Reliability compounds trust
Architecture lets you decide where to use intelligence and where not to.
What Good Architecture Actually Looks Like
1. Explicit State Management
Agents should never “remember” implicitly. Memory must be:
Structured
Queryable
Durable
If step 10 can’t reference step 3, your agent isn’t intelligent—it’s improvising.
2. Deterministic Tool Interfaces
Models reason. Tools execute.
Clean boundaries between the two prevent hallucinated actions and silent failures.
3. Clear Failure Paths
Every system should answer:
What happens when this fails?
Who gets notified?
What context do they receive?
If the answer is “we’ll see,” you don’t have a system—you have a gamble.
4. Human-in-the-Loop by Design
Humans aren’t a fallback. They’re part of the architecture.
The best systems know when to escalate—and do it early, with context.
Why Cheaper Models Often Win
As inference costs fall, model choice becomes less strategic than system design.
A cheaper, faster model inside a robust architecture:
Iterates faster
Scales more predictably
Fails more gracefully
Costs less to debug
This is why many production teams quietly downgrade models after launch—once the architecture does the heavy lifting.
The Uncomfortable Truth
Most “AI failures” are not model failures.
They are:
Missing memory
Poor integration
No guardrails
No observability
No ownership
Upgrading the model doesn’t fix these. It just hides them—temporarily.
Architecture Is Strategy in Disguise
When you choose architecture, you’re making long-term bets:
On how work flows
On how risk is handled
On how humans and machines collaborate
Models will change every 6–12 months.
Your architecture will live for years.
Choose accordingly.
Final Thought: Build Bodies Before Brains
The enterprises that win with AI agents don’t chase intelligence.
They build bodies that can carry it.
Strong spines. Clear nerves. Reliable reflexes.
Once that exists, even modest intelligence becomes powerful.
Without it, genius just trips over its own feet.
Memory Isn’t Optional: How Enterprise AI Agents Fail Without It
Or, why forgetting is the #1 silent killer of AI productivity
Imagine an employee who starts a task, then forgets everything you told them five minutes ago.
Sounds frustrating, right? Now imagine that employee is an AI agent, running financial reports, processing procurement requests, or routing customer issues.
Welcome to the reality for many enterprise AI deployments: memory—or the lack thereof—is the Achilles’ heel of AI agents.
In his January 2026 tutorial, Vasuman Moza, CEO of Varick Agents, emphasizes memory management as a core architectural element. Let’s unpack why this matters and how enterprises can avoid disaster.
The Problem: Forgetful Agents in Enterprise Workflows
AI agents often fail in production not because the model is “dumb,” but because it can’t remember context across steps. Common symptoms include:
Repeating actions or sending duplicate messages
Losing track of client history
Making contradictory decisions mid-process
Escalating issues without context
Without memory, agents behave like a chaotic intern: capable, but unreliable.
Memory Is Architecture, Not Feature
Memory is not an afterthought. It’s foundational, influencing every layer of agent functionality:
Decision consistency: step 10 should recall step 3 decisions
Integration: databases, CRM/ERP, and workflow tools must sync seamlessly
Human-in-the-loop efficiency: humans escalate issues with full context, reducing error resolution time
Vasuman highlights structured memory storage and retrieval as the solution, rather than ad-hoc prompts or temporary session states.
How Poor Memory Kills ROI
Enterprises adopt AI agents to save money and reduce human effort. But memory failures erode ROI quickly:
Rework and duplicated effort increase operational costs
Employee trust in agents drops, lowering adoption
Edge cases handled poorly cause bottlenecks and missed deadlines
In other words, memory issues don’t just frustrate—they cost millions.
Best Practices for Enterprise Agent Memory
1. Persistent, Structured Storage
Store interactions and decisions in a database
Include timestamps, action types, and outcomes
Enable queryable access for future steps
2. Context-Aware Recall
Step 10 can reference previous steps automatically
Agents should infer relevant details without retraining for each session
3. Integrate With Existing Systems
CRM, ERP, ticketing, and analytics platforms should feed and receive memory
Prevents “islands of intelligence”
4. Long-Term Retention
Some decisions may be relevant weeks or months later
Design for both short-term memory (current session) and long-term memory (historical data)
5. Human Oversight With Context
Escalations should include all memory relevant to the decision
Reduces handoff errors and accelerates resolution
Case in Point: Finance and Procurement
Consider an AI agent handling vendor invoices:
Without memory: it may approve duplicate payments or miss prior approvals
With memory: it recalls previous decisions, flags anomalies, and escalates only unique cases
This difference can save millions annually, especially in large enterprises.
The 80/20 Rule and Memory
Memory doesn’t have to cover every conceivable scenario upfront. Apply the 80/20 automation principle:
Automate 80–90% of common cases
Use memory + human-in-the-loop for edge cases
Iterate and expand memory capabilities as workflows evolve
The Bottom Line
Memory isn’t optional for AI agents—it’s mission-critical. Poor memory leads to:
Operational chaos
Loss of trust
Wasted investment
Good memory turns agents from flashy demos into multipliers of enterprise productivity.
As Vasuman points out: build memory from day one, not as a patch later. It’s the spine that turns raw AI capability into consistent, reliable impact.
Custom vs. Generic AI Agents: Why One Size Never Fits in Enterprise Automation
In the rush to adopt AI, many enterprises make a critical mistake: they treat all AI agents like interchangeable tools. They buy off-the-shelf SaaS solutions, expecting instant workflow automation, only to discover months later that these agents handle just 60% of real-world processes—and often create integration headaches.
Vasuman Moza, CEO of Varick Agents, highlights this challenge in his January 2026 tutorial: enterprise AI agents must be custom-built using your organization’s knowledge base and integrated seamlessly into existing systems. Here’s why custom agents are the difference between transformative ROI and expensive “pilot purgatory.”
The Limitations of Generic SaaS Agents
Generic AI tools excel at standardized workflows. They shine in demos, check boxes, and offer slick UIs—but they stumble when:
Workflows vary across departments
Data is siloed in ERP, CRM, or proprietary databases
Human approval and compliance rules are required
The result? Agents that break under real-world complexity, leaving teams frustrated and ROI unrealized.
Custom Agents: Built for Your Enterprise DNA
Custom agents aren’t just configured differently—they’re architected to understand your company’s context. Key advantages include:
Full Workflow Integration
Agents interact directly with ERP, CRM, ticketing, and analytics systems
No need for migration or manual reconciliation
Domain Knowledge Embedding
Use proprietary processes, policies, and historical data
Agents can answer nuanced questions and make context-aware decisions
Human-in-the-Loop for Edge Cases
Escalate complex or unusual cases with full context
Reduces errors and maintains trust in AI
Scalable & Iterative Architecture
Start with high-impact tasks
Expand coverage as workflows evolve, ensuring memory and intelligence grow with your business
The ROI Difference
Let’s take finance/accounting as an example:
Generic agent: automates invoice approvals 60% of the time, still requires human rework for exceptions
Custom agent: automates 80–90% of cases, remembers prior approvals, flags anomalies, and reduces manual intervention dramatically
The difference can translate to millions saved annually—all while increasing operational reliability.
When Generic Might Still Help
Generic SaaS agents aren’t useless—they work well for:
Standardized tasks across many clients (e.g., simple ticket routing)
Rapid prototyping before committing to enterprise-scale automation
Organizations with low workflow complexity
However, even in these cases, embedding memory, human-in-the-loop oversight, and integration hooks is essential for growth.
Practical Steps for Enterprises
Audit Workflows: Identify high-friction, high-value processes for automation.
Map Knowledge Assets: Collect the data, policies, and historical decisions your agents must know.
Start Small: Build a core custom agent handling 80–90% of common cases.
Iterate with Feedback: Expand functionality based on real-world results, not theoretical coverage.
Integrate Deeply: Ensure agents communicate with all enterprise systems and remember prior actions.
Bottom Line
Generic AI agents are like off-the-rack suits—they look nice in the store but rarely fit perfectly. Custom AI agents, on the other hand, are tailored to your organization’s workflows, knowledge, and priorities, delivering measurable productivity gains and lasting ROI.
As Vasuman puts it: “Good architecture with a mediocre model beats a great model with poor architecture every time.”
Enterprises that embrace bespoke AI agents today will see their workflows multiply in efficiency tomorrow.
The 80/20 Automation Rule: How to Maximize AI Agent Impact Without Overengineering
Enterprise AI adoption is full of traps. One of the most common mistakes? Trying to automate every possible workflow scenario before deploying a single agent. The result: delayed launches, frustrated teams, and diminishing returns.
Vasuman Moza, CEO of Varick Agents, addresses this in his January 2026 tutorial: the 80/20 automation principle is the secret to delivering meaningful business impact quickly while maintaining flexibility for future growth. Here’s how enterprises can leverage it.
What is the 80/20 Automation Rule?
The concept is simple but powerful:
Automate 80–90% of routine, predictable tasks reliably.
Route the remaining 10–20% of complex or unusual scenarios to human experts, equipped with full context.
In other words, don’t waste time perfecting edge cases upfront—focus on high-impact automation first.
Why 80/20 Works in the Real World
Speed to Value
Launching agents that handle the core workflows immediately creates ROI.
Teams see tangible benefits within weeks, not months.
Reduced Risk
Human-in-the-loop ensures exceptions don’t derail operations.
Agents learn from edge cases, improving over time without catastrophic failures.
Scalable Architecture
Prioritizing the 80% keeps memory management, data integration, and workflow orchestration simpler.
Adds agility for future feature expansion or multi-agent coordination.
Applying 80/20 Across Enterprise Functions
Finance & Accounting
Automate routine invoice approvals, reconciliation, and reporting.
Escalate unusual transactions to accountants with all contextual data.
Sales Enablement
Auto-generate lead scoring, outreach drafts, and CRM updates.
Flag ambiguous leads for human review, maintaining accuracy and personalization.
HR & IT Support
Handle common requests like password resets, leave approvals, or onboarding checklists.
Route unusual issues to specialists, ensuring employee satisfaction.
Human-in-the-Loop: More Than a Safety Net
The remaining 10–20% of complex cases isn’t just a fallback—it’s a learning engine:
Agents can record decisions made by humans to improve future automation.
Teams gain confidence in agent decisions because edge cases are always reviewed.
Continuous improvement creates a virtuous cycle: better coverage, fewer escalations, more ROI.
Common Pitfalls to Avoid
Overengineering Early: Trying to automate every exception before launch delays benefits.
Ignoring Feedback Loops: Agents must learn from human escalations to improve accuracy.
Neglecting Integration: Edge cases fail when agents don’t have access to all relevant enterprise data.
The Bottom Line
The 80/20 rule turns AI agents into productivity multipliers. It balances speed, reliability, and adaptability—letting enterprises reap significant gains without falling into the trap of trying to build a perfect agent from day one.
As Vasuman notes, “Ship fast, handle the common cases well, and let humans guide the exceptions. That’s how enterprise AI succeeds in production.”
Memory as the Backbone of AI Agents: Why Enterprise Workflows Depend on Recall
In enterprise AI, the devil is in the details—or more accurately, in forgotten details. One of the most common reasons AI agents fail in production is poor memory management. Vasuman Moza, CEO of Varick Agents, highlighted this in his January 2026 tutorial: agents that cannot recall prior interactions or contextual data are prone to errors, inconsistencies, and ultimately, lost trust.
Here’s why memory is not just a feature—it’s the foundation of reliable, high-impact AI agents.
Why Memory Matters
Enterprise workflows are rarely linear. Consider these examples:
Finance: Step 10 of a reconciliation process may depend on decisions made in Step 3. Without memory, the agent can make inconsistent recommendations.
Sales: Understanding client history across multiple interactions ensures accurate lead scoring and outreach personalization.
IT Support: Resolving a complex ticket often requires recalling prior system states, user issues, and past resolutions.
An AI agent that forgets these details is like a rookie employee who has to re-learn everything every morning—frustrating, inefficient, and error-prone.
Principles for Building Memory into AI Agents
Structured Storage
Use databases, knowledge graphs, or vector stores to maintain persistent, queryable context.
Avoid temporary or ephemeral memory that resets between sessions.
Context-Aware Recall
The agent should retrieve relevant past actions, decisions, or conversations dynamically.
Example: In sales, recall prior objection handling to tailor follow-ups automatically.
Long-Term Retention
Ensure that critical workflows, compliance data, and client preferences persist over months or years.
Avoid memory fragmentation that can lead to inconsistent outputs.
Integration with Enterprise Systems
Connect memory to ERP, CRM, or proprietary databases to reduce silos.
This ensures agents operate with full context, not partial or outdated information.
Human-in-the-Loop Enhances Memory
Memory isn’t just for the agent—it’s also for humans:
When complex cases are escalated, the agent provides full context, reducing back-and-forth and mistakes.
Human interventions become training signals, improving the agent’s future memory recall and decision-making.
Common Pitfalls to Avoid
Treating Memory as Optional: Many teams assume the agent can “figure it out” on each interaction—this leads to catastrophic errors in workflows.
Fragmented Context: Splitting memory across systems without unified access results in incomplete recall.
Ignoring Updates: Memory must be continuously updated to reflect changes in processes, clients, or regulations.
Business Impact of Robust Memory
Consistency: Decisions remain aligned across multiple interactions, departments, and timeframes.
Efficiency: Reduces repetitive human intervention and accelerates workflow completion.
Trust: Users feel confident that agents “remember” relevant context, improving adoption and retention.
ROI: Better recall directly translates to higher productivity and measurable savings.
The Bottom Line
Memory is the unsung hero of enterprise AI. Without it, even the smartest agents stumble in real-world applications. By designing agents with structured, context-aware, and integrated memory from day one, companies can turn AI from a demo novelty into a reliable productivity multiplier.
As Vasuman emphasizes, “Step 10 must recall Step 3. Without memory, there’s no enterprise-grade AI—it’s just a flashy toy.”
Why Custom AI Agents Outperform Generic SaaS Tools in the Enterprise
When companies invest in AI, the temptation is often to adopt off-the-shelf SaaS solutions. They promise quick wins, low upfront costs, and a “plug-and-play” experience. But as Vasuman Moza, CEO of Varick Agents, emphasized in his January 2026 tutorial, generic tools rarely deliver full enterprise impact. The reason is simple: enterprises are messy, workflows are complex, and every business has its own DNA.
Here’s why custom AI agents are the secret weapon for companies that want real ROI.
1. Tailored to Your Unique Workflows
Generic SaaS tools are designed to fit the average company, not yours. They often cover only 60% of workflows and require process compromises or forced integrations. Custom AI agents, by contrast:
Integrate seamlessly with existing ERP, CRM, and proprietary systems.
Understand your data structures, naming conventions, and legacy processes.
Automate end-to-end workflows rather than just isolated tasks.
Think of it like comparing a ready-made suit to a tailored one: the off-the-rack option might fit, but it won’t empower you the way a custom-cut suit does.
2. Designed for Real-World Chaos
Enterprises are full of exceptions, edge cases, and unpredictable inputs. Generic AI tools are brittle—they often break when reality deviates from ideal demo scenarios. Custom agents:
Handle 80-90% of common scenarios reliably.
Escalate the remaining cases to human-in-the-loop workflows.
Adapt over time as processes evolve, using dynamic architecture and persistent memory.
The result is not a fragile demo—it’s a production-ready agent that multiplies productivity.
3. Rapid ROI and Measurable Impact
Custom AI agents are built with business value in mind, not just AI innovation. They can:
Reduce month-end closes or procurement cycles by 50% or more.
Generate millions in client savings within months of deployment.
Maintain 100% client retention through predictable, auditable workflows.
Generic SaaS solutions often provide incremental automation, but rarely deliver step-change efficiency that justifies enterprise-scale investment.
4. Flexibility for Growth and Expansion
Businesses evolve. New departments, acquisitions, or regulatory requirements can make rigid SaaS tools obsolete. Custom AI agents:
Scale easily across geographies, business units, and workflow types.
Allow modular upgrades, integrating new models, tools, or compliance features.
Enable multi-agent orchestration for complex, cross-department workflows.
In short, they future-proof your automation strategy.
5. Competitive Differentiation
Companies that rely solely on SaaS risk commoditization of their workflows. Custom AI agents provide a strategic advantage:
Workflows become proprietary assets.
Knowledge embedded in agents can’t be copied by competitors.
Employees can focus on high-value, creative work, while agents handle operational grunt work.
It’s the difference between having a generic tool and owning an intelligent workforce.
Bottom Line
For enterprise-scale impact, custom AI agents outperform generic SaaS tools every time. They combine deep integration, adaptability, measurable ROI, and future-ready scalability. Vasuman’s key insight is clear: if you want AI to be a real productivity multiplier, you need to stop thinking of it as a “nice-to-have app” and start designing it as a strategic enterprise partner.
Memory Matters: Why Enterprise AI Agents Fail Without Robust Context Management
One of the most overlooked aspects of enterprise AI agent design is memory—the ability for an agent to remember past interactions, decisions, and contextual data across workflows. As Vasuman Moza, CEO of Varick Agents, highlighted in his January 2026 tutorial, poor memory is one of the biggest reasons AI agents collapse in production, even when demos look flawless.
Here’s why memory is critical and how enterprises can build agents that retain context, learn, and deliver real-world ROI.
1. Enterprise Workflows Are Non-Linear
Unlike consumer chatbots, which handle simple queries, enterprise workflows are complex and iterative. A sales agent may need to track:
Customer interactions spanning weeks.
Approvals and exceptions across multiple departments.
Regulatory and compliance constraints tied to specific actions.
Without memory, an agent “forgets” key details, leading to inconsistent decisions, errors, and loss of trust. For example, if an AI forgets that a client requested a custom discount in step 3 of a workflow, it might approve a conflicting offer in step 10—jeopardizing revenue and client relationships.
2. Memory as Core Architecture, Not an Afterthought
Many AI projects treat memory as optional. Vasuman advises: build it into the foundation. This means:
Structured storage: Integrate with databases, ERP systems, and CRM tools to persist critical data.
Step-by-step recall: Ensure agents can retrieve past decisions or actions (e.g., step 10 recalls step 3).
Contextual reasoning: Enable agents to make informed choices based on accumulated knowledge, not just the immediate input.
Think of memory as the agent’s nervous system—without it, the AI can’t react intelligently to real-world stimuli.
3. Long-Term Retention Enables Strategic Insights
Memory isn’t just about avoiding errors—it’s a strategic lever. Agents that retain historical context can:
Detect patterns across clients, departments, or regions.
Provide predictive recommendations for sales, procurement, or compliance.
Accelerate onboarding for new team members by carrying forward accumulated knowledge.
This transforms agents from reactive tools into proactive teammates that drive measurable business value.
4. Human-in-the-Loop as Memory Failsafe
Even the best memory systems have gaps. For edge cases or ambiguous scenarios, a human-in-the-loop framework ensures smooth operation:
Agents flag unusual cases for review.
Humans provide context that the agent can store for future use.
The system improves over time, reinforcing its memory and decision-making capabilities.
This hybrid approach balances efficiency with risk management and accountability.
5. Memory + Custom Architecture = Enterprise ROI
Vasuman emphasizes that good memory with mediocre models beats bad memory with perfect models. Why? Because enterprise impact depends on reliable, consistent decision-making, not just advanced language understanding. Agents that remember:
Who approved what.
Which processes were followed.
How exceptions were handled.
…become trusted extensions of the workforce, capable of automating complex operations with measurable ROI.
Key Takeaways
Memory is non-negotiable for agents that handle real enterprise workflows.
Integrate memory from day one, leveraging structured databases and persistent storage.
Use human-in-the-loop to handle edge cases and enrich agent memory.
Memory transforms agents into strategic multipliers, not just task executors.
In short, if you want your AI agents to survive and thrive in production, invest in memory architecture as diligently as you do in models. It’s the difference between a shiny demo and a fully autonomous enterprise teammate.
Shipping Enterprise AI Agents Fast Without Breaking Them: A Pragmatic Guide
In the world of enterprise AI, speed is often mistaken for recklessness. Yet, as Vasuman Moza, CEO of Varick Agents, highlighted in his January 2026 tutorial, shipping fast doesn’t mean shipping broken agents. The challenge is to move quickly without sacrificing reliability, adaptability, or ROI.
Here’s a blueprint for delivering AI agents that hit the ground running—and stay in production.
1. Start Small, Ship Fast
The first rule of enterprise agent deployment is start with core workflows, not the entire system. Focus on automating 80–90% of common cases:
Begin with high-frequency, low-variability processes like invoice approvals, CRM updates, or customer ticket triage.
Avoid over-engineering for rare edge cases upfront—they can be handled later via human-in-the-loop mechanisms.
Launch small iterations to capture real feedback, not hypothetical scenarios.
This approach allows teams to learn from production data quickly, reducing costly misfires in complex environments.
2. Dynamic, Not Static Architecture
Many AI projects fail because agents are built like rigid APIs: inflexible, unable to adapt to unexpected inputs. Successful enterprise agents require:
Modular design: Separate decision logic, memory, and execution layers for easier updates.
Real-time error handling: Agents should detect anomalies, self-correct, or route to humans without halting the workflow.
Scalable integrations: Ensure smooth connections with ERP, CRM, or proprietary databases, so the agent doesn’t break under operational load.
Think of the architecture as a living organism, capable of evolving as workflows change and complexity grows.
3. Feedback Loops Are Your Lifeline
The fastest way to improve an enterprise agent is through continuous feedback:
Collect metrics on task completion, errors, and user interventions.
Build dashboards for business owners to visualize agent performance.
Use feedback to refine rules, memory, and multi-agent coordination.
By iterating in cycles, agents evolve from demo-ready prototypes to trusted enterprise teammates.
4. Prioritize Memory and Context
Shipping fast without memory is like running a marathon blindfolded. Agents must retain context across steps and sessions:
Persist important details in structured storage.
Ensure agents recall prior actions for coherent decision-making.
Use human-in-the-loop as a fallback for unusual scenarios.
This guarantees consistency, reliability, and user trust from day one.
5. Automation With Guardrails
Rapid deployment must be paired with risk mitigation:
Implement thresholds for actions that could impact finance, compliance, or customer relations.
Log every decision for auditing and traceability.
Introduce gradual escalation mechanisms for high-stakes workflows.
These guardrails transform agents from potential liabilities into safe, productive collaborators.
6. Measure ROI Early and Often
Enterprise leaders won’t forgive agents that fail silently. Demonstrate value from the start by:
Tracking time saved, error reduction, and cost avoidance.
Quantifying improvements in client workflows, e.g., reducing month-end close by 50%.
Sharing success stories internally to build confidence and buy-in for expansion.
Early wins create momentum for scaling to additional workflows and departments.
Key Takeaways
Start small—focus on core processes, then iterate.
Build dynamic, modular architectures that survive real-world variability.
Establish feedback loops to continuously improve performance.
Embed memory and context from day one.
Deploy guardrails to mitigate risk in critical workflows.
Measure ROI to prove value and drive adoption.
In short, speed and reliability aren’t mutually exclusive—they’re complementary. With the right strategy, enterprises can ship AI agents fast, scale them safely, and multiply productivity without the heartbreak of post-launch failures.
To @vasuman @varickai Varick Agents: The Manhattan Project for Enterprise AI https://t.co/LcgAPhojeE What you need is an Executive Chairperson like me to see rocket growth.
— Paramendra Kumar Bhagat (@paramendra) January 23, 2026
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