AI Agents: The Key to a Hundredfold Increase in Business Scale
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AI Agents: The Key to a Hundredfold Increase in Business Scale
A Practical Guide to Building Usable Agents
Navigating Web3 tides with focused insightsAuthor: vas
Translation: AididiaoJP, Foresight News
AI is not magic, but it’s also far from “set up an AI program, automate everything, sit back and collect profits.” Most people don’t actually understand what AI really is.
And those who do understand (fewer than 5%) often try to build their own systems—and frequently fail. Agents hallucinate, forget where they are mid-task, or mistakenly call tools when they shouldn’t. It runs perfectly in demos, then crashes immediately in production.
I’ve been deploying AI systems for over a year. My software career began at Meta, but six months ago I left to found a company focused on deploying production-ready AI agents for enterprises. Today, we’re generating $3 million in annual recurring revenue—and growing. Not because we’re smarter than others, but because we failed repeatedly, learned through trial and error, and eventually cracked the formula for success.
Here’s everything I’ve learned while building truly usable agents. Whether you're a beginner, an expert, or somewhere in between, these lessons should apply.
Lesson One: Context Is Everything
This might sound obvious—you’ve probably heard it before. But precisely because it's so critical, it bears repeating. Many believe building agents is just about connecting tools: pick a model, grant database access, and walk away. This approach fails instantly, for several reasons:
Agents don’t know what matters. They can't recall what happened five steps ago—they only see the current step, guess what comes next (and often guess wrong), and end up relying on luck.
Context is often the sole difference between a million-dollar agent and a worthless one. Focus your efforts and optimization here:
What the agent remembers: Not just the current task, but the full history that led to this point. For example, when handling an invoice discrepancy, the agent must know: how the issue was triggered, who submitted the original invoice, which policy applies, and how similar issues with this supplier were resolved previously. Without this history, the agent is guessing—worse than useless. Human workers would have solved the problem already. This explains why some complain, “AI is so hard to use.”
How information flows: When you have multiple agents or a multi-step process, information must be accurately passed between stages without loss, corruption, or misinterpretation. The agent responsible for classifying requests must deliver clean, structured context to the agent solving the problem. If handoffs aren’t rigorous, downstream processes break. This means every stage needs verifiable, structured inputs and outputs. An example is the /compact feature in Claude Code, which preserves context across different LLM sessions.
The agent’s understanding of your business domain: An agent reviewing legal contracts must understand which clauses are critical, risk implications, and actual company policies. You can’t just dump documents and expect it to figure out what matters—that’s your responsibility. And part of that responsibility includes providing resources in a structured way so the agent gains real domain expertise.
Poor context management looks like: An agent repeatedly calls the same tool because it forgot prior results; invokes the wrong tool due to incorrect input; makes decisions contradicting earlier steps; or treats every task as brand new, ignoring clear patterns from past similar tasks.
Good context management enables the agent to act like an experienced business expert: making connections between disparate pieces of information without being explicitly told how they relate.
Context is the key differentiator between agents that “only demo well” and those that run reliably in production and deliver real results.
Lesson Two: AI Agents Are Force Multipliers
Wrong mindset: “Now we won’t need to hire people.”
Right mindset: “Now three people can do the work of fifteen.”
Agents will eventually replace certain labor—it’s self-deception to deny this. But the positive side is: agents don’t replace human judgment; they eliminate the friction around it—like searching for data, collecting documents, cross-referencing, formatting, task routing, follow-up reminders, etc.
For example: Finance teams still make decisions on exceptions, but with agents, they no longer spend 70% of closing week hunting down missing receipts. Instead, they use that time to actually resolve issues. The agent does all the groundwork; humans handle final approval. Based on my client experience, companies rarely cut headcount as a result. Employee roles shift—from repetitive manual work to higher-value tasks—at least for now. Long-term, this may change as AI advances.
The companies that truly benefit from agents aren’t those trying to remove humans from workflows, but those realizing employees spend most of their time on “preparatory work,” not value creation.
Design agents this way, and you no longer obsess over “accuracy.” Let agents do what they’re good at, and let humans focus on what they do best.
It also means faster deployment. Agents don’t need to handle every edge case—just manage common scenarios well, and escalate complex or unusual ones to humans—with enough context for quick resolution. At least, that’s the right approach today.
Lesson Three: Memory and State Management
How an agent stores information within a task and across tasks determines its scalability.
There are three common patterns:
Standalone Agent: Handles the entire workflow from start to finish. Easiest to build since all context resides in one place. But as workflows grow longer, state management becomes challenging: the agent must remember decisions made in step three when executing step ten. If the context window fills up or memory structure is flawed, later decisions lack support from earlier information, leading to errors.
Parallel Agents: Multiple agents work simultaneously on different parts of the same problem. Faster, but introduces coordination challenges: How are results merged? What if two agents reach conflicting conclusions? Clear protocols are needed to integrate information and resolve conflicts. Often requires a “referee”—a human or another LLM—to handle disputes or race conditions.
Collaborative Agents: Work sequentially, passing tasks along. Agent A classifies, passes to B for research, then to C for execution. Ideal for clearly staged workflows, but handoff points are prone to failure. Information learned by Agent A must be delivered to Agent B in a directly usable format.
Most people’s mistake is treating these as mere implementation options. In reality, they’re architectural decisions that directly define your agent’s capability ceiling.
For instance, if you’re building an agent for sales contract approvals, you must decide: Should one agent handle the entire process? Or design a routing agent that assigns tasks to specialized agents for pricing review, legal compliance, executive sign-off? Only you know your actual business processes—and ideally, you’ll teach them to your agents.
Which to choose depends on the complexity of each stage, how much context must be transferred, and whether real-time collaboration or sequential execution is required.
Choose the wrong architecture, and you could spend months debugging issues that aren’t bugs at all—just a mismatch between your design, the problem, and the solution.
Lesson Four: Intercept Exceptions Proactively, Don’t Report Them Afterward
When building AI systems, many people’s first instinct is: “Let’s build a dashboard to show what’s happening.” Please—stop building dashboards.
Dashboards are useless.
Your finance team already knows invoices are missing documents. Your sales team already knows contracts are stuck in legal review.
Agents should intercept problems the moment they occur, escalate them to the right person, provide all necessary resolution context, and trigger immediate action.
Invoice received but documentation incomplete? Don’t just log it into a report. Immediately flag it, identify exactly who needs to provide what, and send the complete context (supplier, amount, applicable policy, specific missing items) directly to that person. Block the transaction from posting until resolved. This step is crucial—otherwise, problems “leak” across the organization, and by the time you notice, it’s too late.
Contract approval stalled for over 24 hours? Don’t wait for the weekly meeting. Automatically escalate it, attach full transaction details, so the approver can decide quickly without digging through systems. Create urgency.
Vendor missed a milestone deadline? Don’t wait for someone to notice. Automatically trigger contingency plans and initiate response workflows before anyone realizes there’s a problem.
The job of your AI agent is to make problems impossible to ignore—and effortless to solve.
Expose issues directly—don’t bury them in dashboards.
This is the opposite of how most companies use AI: they use it to “see” problems, but you should use it to “force” problems to be solved—fast. Only once resolution rates near 100% should you consider building a dashboard to monitor performance.
Lesson Five: The Economics of AI Agents vs. Generic SaaS
There’s a reason enterprises keep buying SaaS tools that nobody uses.
SaaS is easy to procure: there’s a demo, a quote, a checkbox on the requirements list. Once approved, people feel progress has been made (even when it hasn’t).
Buying AI-powered SaaS is worst of all: it just sits there. It doesn’t integrate into real workflows, becoming yet another system to log into. You’re forced to migrate data, and a month later, you’ve just added another vendor to manage. Twelve months later, it’s abandoned—but you can’t get rid of it due to high switching costs, resulting in “technical debt.”
Custom AI agents built on your existing systems avoid this entirely.
They operate within tools you already use, create no new platforms, and simply accelerate existing work. Agents handle tasks; humans only see outcomes.
The real cost comparison isn’t “development cost vs. license fee,” but a simpler logic:
SaaS accumulates “technical debt”: Each new tool adds another integration to maintain, another system that will eventually become obsolete, another vendor that might get acquired, pivot, or shut down.
Custom agents accumulate “capability”: Every improvement makes the system smarter; every new workflow expands possibilities. Investment compounds over time instead of depreciating.
That’s why I’ve said consistently over the past year: generic AI SaaS has no future. Industry data confirms this: most companies purchasing AI SaaS discontinue use within six months, seeing zero productivity gains. The real beneficiaries are companies with custom agents—whether built in-house or by third parties.
This is why early adopters of agents gain long-term structural advantages: they’re building infrastructure that grows stronger over time. Others are merely renting tools they’ll eventually have to replace. In an era of rapid monthly changes, wasting even one week represents a major setback for your product roadmap and overall business.
Lesson Six: Deploy Fast
If your AI agent project takes a year to launch, you’ve already lost.
Plans don’t survive reality. Your designed workflow likely won’t match actual operations, and the edge cases you didn’t anticipate are often the most important. Twelve months from now, the AI landscape could be unrecognizable—your carefully built system might already be obsolete.
You must enter production within three months—maximum.
In this age of information overload, true competence lies in knowing how to effectively leverage information and collaborate with it—not fight against it. Get hands-on: process real tasks, make real decisions, leave traceable records.
The most common problem I see: internal development teams estimate a 3-month AI project will take 6–12 months. Or worse—they say “three months” but then delay indefinitely due to various “unexpected issues.” It’s not entirely their fault; AI is inherently complex.
So you need engineers who truly understand AI: those who grasp how AI scales, have seen real-world failures, and clearly understand AI’s capabilities and limits. Too many “half-knowledge” developers believe AI can do anything—far from the truth. If you’re a software engineer aiming to enter enterprise AI, you must deeply understand AI’s practical boundaries.
To Summarize
Building usable agents comes down to these key points:
Context is king: An agent without good context is just an expensive random number generator. Prioritize smooth information flow, persistent memory, and embedded domain knowledge. People used to mock “prompt engineers,” but now “context engineers” are its 2.0 evolution.
Design for augmentation, not replacement: Let humans do what humans do best, and let agents clear the path so humans can focus.
Architecture matters more than model choice: Whether you use standalone, parallel, or collaborative agents is far more important than which model you select. Get the architecture right first.
Intercept and resolve—don’t report and review: Dashboards are graveyards for problems. Build systems that force rapid resolution.
Launch fast, iterate continuously: The best agent is the one already running in production and improving constantly—not the one still in design. (And watch your timeline closely.)
Everything else is detail.
The technology is ready—but you might not be.
Understand this, and you can scale your business 100x.
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@vasuman
