AI intelligent agents: the key to a hundredfold increase in business scale

AI isn’t magic, nor is it as simple as “just set up an AI program and watch the profits roll in.” The reality is, most people don’t truly understand what AI is.

The handful who do—less than 5%—often try to build their own solutions and end up failing. Agents can hallucinate, lose track of task progress, or mistakenly trigger tools at the wrong time. While the demo may run flawlessly, everything falls apart in production.

I’ve spent over a year deploying AI programs. My career began at Meta, but six months ago, I left to launch a company focused on deploying production-grade AI agents for enterprises. Our annual recurring revenue has reached $3 million and continues to grow. This isn’t because we’re smarter—it’s the result of repeated trial and error, countless failures, and finally cracking the formula for success.

Here’s what I’ve learned about building agents that actually work. No matter your experience level—beginner, expert, or in between—these insights are for you.

Lesson One: Context Is Everything

This may sound obvious, and you’ve likely heard it before. But its importance can’t be overstated. Many believe building agents means stringing together a few tools: pick a model, open up database access, and let it run. This approach fails immediately, for several reasons:

Agents don’t understand priorities. They forget what happened several steps ago, only see the present, and then guess what’s next—often incorrectly—leaving outcomes to chance.

Context is the true differentiator between million-dollar agents and worthless ones. Focus on optimizing these areas:

Agent memory: Not just the current task, but the full history leading up to it. For instance, when handling invoice anomalies, the agent needs to know how the exception occurred, who submitted the invoice, which policy applies, and how previous issues with the supplier were resolved. Without this, the agent is just guessing—worse than having no agent at all. A human would likely have solved the problem already. This is why people complain that “AI is so hard to use.”

Information flow: With multiple agents or multi-step processes, information must transfer accurately between stages—no loss, corruption, or misinterpretation. The agent that classifies requests must deliver clean, structured context to the agent solving the issue. If the handoff isn’t precise, everything downstream unravels. That means every step requires verifiable, structured inputs and outputs. For example, Claude Code’s /compact feature passes context between LLM sessions.

Domain expertise: An agent reviewing legal contracts needs to know which clauses matter, how to assess risks, and the company’s actual policies. You can’t just dump documents and expect the agent to figure it out—that’s your job. You must provide resources in a structured way so the agent truly gains domain knowledge.

Poor context management looks like this: agents repeatedly call the same tool because they forgot the answer, invoke the wrong tool due to bad information, make decisions that contradict previous steps, or treat every task as brand new, ignoring clear patterns from similar past tasks.

Good context management lets agents operate like seasoned business experts—connecting information without explicit instructions.

Context is what separates “demo-only” agents from those that actually deliver in production.

Lesson Two: AI Agents Multiply Output

The wrong idea: “With this, we won’t need to hire.”

The right idea: “With this, three people can do what used to take fifteen.”

Agents will eventually replace some manual work—denying that is wishful thinking. The upside: agents don’t replace human judgment, but eliminate the friction around it—searching for data, collecting info, cross-checking, formatting, distributing tasks, sending reminders, and more.

Take finance teams: they still make decisions about anomalies, but with agents, they don’t spend 70% of closing week hunting down missing documents. That 70% goes to actually solving problems. Agents handle the groundwork; humans do the final review. In my client work, companies aren’t laying off staff. Employees shift from tedious manual work to higher-value tasks—at least for now. Long-term, as AI evolves, this may change.

The companies that truly benefit aren’t those trying to remove humans, but those who realize most employee time is spent on “setup work” rather than creating value.

Design agents with this in mind and accuracy rates become less of an obsession: agents do what they excel at; people do what they do best.

This lets you deploy faster. Agents don’t need to handle every edge case—just cover the common scenarios and hand off complex exceptions to humans with enough context for quick resolution. For now, that’s the right approach.

Lesson Three: Memory and State Management

How agents retain information within and across tasks determines scalability.

Three common patterns:

Standalone agent: Manages the entire workflow start to finish. Easiest to build, since all context is centralized. But as workflows grow, state management becomes tough—agents must remember decisions from step three and apply them at step ten. If the context window is full or the memory structure is off, late-stage decisions lack early-stage context, causing errors.

Parallel agents: Handle different parts of a problem simultaneously. Faster, but introduces coordination challenges—how do you merge results? What if agents disagree? You need clear protocols to integrate information and resolve conflicts, often with a “referee” (human or LLM) for disputes or race conditions.

Collaborative agents: Pass tasks sequentially. Agent A classifies, B researches, C executes. Good for workflows with clear stages, but handoffs are the weak point—A’s insights must transfer to B in a usable format.

The common mistake: treating these as “implementation plans.” They’re actually architecture choices that define your agent’s capabilities.

For example, building an agent for sales contract approvals means deciding: should one agent do it all, or should a routing agent delegate to specialists for pricing, legal, and executive review? Only you know your actual business flow—and you need to teach it to your agent.

How to choose? Depends on each stage’s complexity, how much context must be passed, and whether you need real-time collaboration or sequential execution.

Pick the wrong architecture, and you’ll spend months debugging what aren’t bugs—they’re mismatches between your design, your problem, and your solution.

Lesson Four: Proactively Intercept Exceptions—Don’t Just Report Them

Many people’s first instinct when building AI systems is to create a dashboard to show what’s happening. Please—stop building dashboards.

Dashboards don’t help.

Your finance team already knows about missing receipts, and sales already knows which contracts are stuck in legal.

Agents should intercept problems as they happen, hand them off directly to the right person, and provide all relevant information for immediate resolution.

Got an invoice missing documents? Don’t just log it. Flag it instantly, identify what’s missing, and send the issue—complete with context (supplier, amount, policy, specifics)—to the responsible party. Block the transaction until resolved. This step is critical; otherwise, problems leak throughout the organization, and you’ll be too late to fix them.

Contract approval stalled for 24 hours? Don’t wait for the weekly meeting. Auto-escalate with transaction details so the approver can make a decision fast—no need to dig through systems. Create urgency.

Supplier missed a milestone? Don’t wait for someone to notice. Trigger emergency protocols automatically before anyone realizes there’s a problem.

Your agent’s job is to make problems impossible to ignore and easy to solve.

Expose issues directly—not just through dashboards.

This is the opposite of how most companies use AI: they use it to “see” problems, but you should use it to “force” solutions—fast. When your resolution rate nears 100%, then consider a dashboard.

Lesson Five: AI Agents vs. Generic SaaS—The Economics

There’s a reason companies keep buying SaaS tools that nobody uses.

SaaS is easy to buy: demo, quote, checkbox on the requirements list. Someone approves it and thinks progress is made—though that’s rarely true.

The biggest problem with AI SaaS: it just sits there. It doesn’t integrate with real workflows and becomes another login. You’re forced to migrate data, and in a month, it’s just another vendor to manage. After a year, it’s abandoned, but switching costs keep it around—creating “technical debt.”

Custom agents built on your current systems avoid these issues.

They run inside your existing tools, don’t introduce new platforms, and help you work faster. Agents do the work; humans review results.

The real cost comparison isn’t “development vs. license fees”—it’s much simpler:

SaaS creates technical debt: every new tool means more integrations to maintain, another soon-to-be-obsolete system, and a vendor that may get acquired, pivot, or shut down.

Building your own agents builds capability: every improvement makes the system smarter, every new workflow expands what’s possible. Investment compounds, not depreciates.

I’ve said for a year: generic AI SaaS has no future. Industry data backs this up—most companies abandon AI SaaS within six months and see zero productivity gains. Real AI value comes from custom agents, whether built in-house or by a third party.

This is why early adopters of agents gain long-term structural advantages—they’re building infrastructure that grows stronger. Others are just renting tools they’ll have to replace. In a field that changes monthly, wasting even a week is a major setback for your roadmap and business.

Lesson Six: Deploy Fast

If your AI agent project takes a year to launch, you’ve already lost.

Plans can’t keep up with change. Your workflow design probably doesn’t match reality, and the edge cases you missed will be the most important. A year from now, AI may be unrecognizable—your project could be obsolete.

Three months, max—get into production.

In today’s information-saturated world, real ability is knowing how to use information effectively and collaborate with it. Get things done: process real tasks, make real decisions, leave an auditable trail.

The most common issue I see: internal teams estimate three-month AI projects as six to twelve months. Or worse—promise three months, then delay endlessly with “unexpected reasons.” It’s not all their fault; AI is genuinely complex.

That’s why you need engineers who truly understand AI—they know how to scale it, have seen real-world problems, and understand its strengths and limits. There are too many “half-baked” developers who think AI can do anything—far from the truth. If you’re a developer aiming for enterprise AI, you must master its practical boundaries.

Summary

Here’s what matters for usable agents:

Context is everything: Without robust context, an agent is just an expensive random number generator. Nail information flow, persistent memory, and domain knowledge embedding. “Prompt engineering” was the old joke—now “context engineering” is version 2.0.

Design for enhancement, not replacement: People should do what they’re best at; agents should clear the way for focus.

Architecture trumps model choice: Deciding between standalone, parallel, or collaborative agents matters far more than picking a model. Get the architecture right.

Intercept and solve, not just report and review: Dashboards are problem graveyards. Build systems that force rapid resolution.

Deploy quickly, iterate relentlessly: The best agent is already running and improving—not stuck in design. (And watch your deadlines.)

Everything else is detail.

The tech is ready, but you might not be.

Understand this, and you can scale your business 100x.

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