AI intelligent entity, the key to a hundredfold increase in business scale

Writing by: vas

Compiled by: AididiaoJP, Foresight News

AI is not magic, but it’s also not simple enough to just “set up an AI program, automate everything, and wait for the profits.” Most people actually don’t fully understand what AI really is.

And those who do (less than 5%) try to build their own, often ending in failure. Intelligent agents can hallucinate, forget what step they are on midway, or call the wrong tools when they shouldn’t. They run perfectly in demos, but crash immediately once in production.

I’ve been deploying AI programs for over a year. My software career started at Meta, but six months ago I left to start a company that specializes in deploying production-ready AI agents for enterprises. Now, our recurring annual revenue has reached $3 million, and it’s still growing. This isn’t because we’re smarter than others; it’s because we’ve repeatedly tried, failed, and finally figured out the formula for success.

Below is everything I’ve learned during the process of building truly usable intelligent agents. Whether you’re a beginner, an expert, or somewhere in between, these insights should apply.

Lesson One: Context is Everything

This may sound obvious, and you might have heard it before. But because it’s so important, it bears repeating. Many think building an agent is just connecting various tools: pick a model, open database permissions, and then you’re done. This approach will fail immediately for several reasons:

The agent doesn’t know what’s important. It can’t see what happened five steps ago, only the current step, and then guesses what to do next (often wrongly), relying on luck.

Context is often the fundamental difference between a million-dollar agent and a worthless one. You need to focus on and optimize these aspects:

What the agent remembers: not just the current task, but the full history that led to the current state. For example, when handling invoice anomalies, the agent needs to know: how was the anomaly triggered, who submitted the original invoice, which policy applies, how was the last issue with this vendor handled. Without this history, the agent is just guessing blindly, which is worse than having no AI at all. Because if humans handled it, the problem might have been solved long ago. This also explains why some complain, “AI is really hard to use.”

How information flows: when you have multiple agents or one agent handling multiple steps, information must be accurately passed between stages without loss, corruption, or misinterpretation. The request classification agent must pass clean, structured context to the problem-solving agent. If handoffs are sloppy, downstream gets messy. This means every step needs verifiable, structured input and output. An example is Claude Code’s /compact feature, which can transfer context between different LLM sessions.

The agent’s understanding of the business domain: an agent reviewing legal contracts must know which clauses are critical, risk estimates, and the company’s actual policies. You can’t just dump a bunch of documents and expect it to figure out the key points—that’s your responsibility. Your responsibility also includes providing resources in a structured way so the agent truly has domain knowledge.

Poor context management manifests as: the agent repeatedly calls the same tool because it forgets it already got an answer; or calls the wrong tool due to receiving incorrect info; or makes contradictory decisions to earlier steps; or treats every task as entirely new, ignoring obvious patterns from similar past tasks.

Good context management makes the agent like an experienced business expert: it can connect different pieces of information without you explicitly telling it how they relate.

Context is the key to distinguishing “demo-only” agents from “production-ready” ones that actually deliver results.

Lesson Two: AI Agents Are Productivity Multipliers

Misconception: “With it, we don’t need to hire anyone anymore.”

Correct view: “With it, three people can do what fifteen used to do.”

AI agents will eventually replace some human labor, and denying this is self-deception. But the positive side is: AI doesn’t replace human judgment; it eliminates friction around human decision-making—like data lookup, data collection, cross-referencing, formatting, task distribution, follow-up reminders, etc.

For example: finance teams still need to make decisions on anomalies, but with AI, they no longer spend 70% of their closing week searching for missing documents. Instead, they can use that 70% to actually solve the problem. The AI handles all the basic work, humans only do the final approval. Based on my experience serving clients, the reality is: companies won’t lay off staff because of this. Employees’ roles shift from tedious manual tasks to more valuable work—at least for now. Of course, in the long run, as AI advances further, this could change.

The companies that truly benefit from AI aren’t those just trying to cut out humans from processes, but those who realize: most employees spend their time on “paving work,” not on the parts that create real value.

Design your AI with this in mind, and you won’t need to obsess over “accuracy”: let the agent do what it’s good at, and humans focus on what they’re best at.

This also means faster deployment. You don’t need the agent to handle every extreme case; just ensure it handles common scenarios well, and pass complex anomalies to humans—with enough context to resolve quickly. At least, this is the approach for now.

Lesson Three: Memory and State Management

How an agent retains information within a task and across tasks determines whether it can scale.

There are three common modes:

Independent Agents: handle entire workflows from start to finish. This is the easiest to set up because all context is in one place. But as workflows lengthen, state management becomes challenging: the agent must remember decisions made at step three when it reaches step ten. If the context window is full or the memory structure is flawed, later decisions lack early info, leading to errors.

Parallel Agents: work on different parts of the same problem simultaneously. Faster, but introduces coordination issues: how to merge results? What if two agents give conflicting conclusions? Clear protocols are needed to integrate info and resolve conflicts. Usually, a “referee” (a human or another LLM) is introduced to handle disputes or race conditions.

Collaborative Agents: pass work sequentially. Agent A classifies, then passes to B for research, then to C for execution. This pattern suits workflows with clear stages, but handoff points are most prone to issues. The output from A must be in a format B can directly use.

Most people mistake these modes as “implementation solutions.” In reality, they are architectural decisions that directly determine your agent’s capabilities.

For example, if you want to build an agent for sales contract approval, you must decide: should one agent handle the entire process? or design a routing agent that distributes tasks to specialized agents for pricing review, legal review, executive approval, etc.? Only you understand the actual business process, and I hope you can teach these workflows to your agents.

How to choose? Depends on the complexity of each stage, how much context needs to be passed, and whether stages require real-time collaboration or sequential execution.

Wrong architecture can mean months of debugging non-bug issues—an architecture mismatch between your design, the problems you want to solve, and your solution.

Lesson Four: Proactively Intercept Exceptions, Not Just Report Them

When building AI systems, many first think: “Let’s make a dashboard to display info and let everyone see what’s happening.” Please, stop making dashboards.

Dashboards are useless.

Your finance team already knows about missing invoices; sales already knows some contracts are stuck in legal.

AI should directly intercept issues when they occur, and hand them off to the right person for resolution, providing all necessary info immediately.

Invoice received but incomplete? Don’t just log it in a report. Mark it immediately, clarify who needs to provide what materials, and pass the issue along with full context (vendor, amount, applicable policy, what’s missing). Also, block the transaction from posting until the issue is resolved. This step is critical; otherwise, problems will “leak” throughout the organization, making it impossible to fix in time.

Contract approval pending over 24 hours? Don’t wait for the weekly meeting. Auto-escalate with transaction details so approvers can decide quickly without hunting through systems. Urgency is key.

Vendor misses a milestone? Don’t wait for someone to notice. Trigger an emergency response automatically, initiating the response process before anyone realizes there’s a problem.

Your AI agent’s role is: make issues impossible to ignore, and make resolving them extremely easy.

Expose problems directly, not indirectly through dashboards.

This is the opposite of how most companies use AI: they use AI to “see” problems, but you should use AI to “force” solutions, and do it fast. Once the problem resolution rate approaches 100%, then consider making dashboards.

Lesson Five: AI Agents vs. General SaaS Economics

Companies keep buying unused SaaS tools for a reason.

SaaS is easy to procure: demos, quotes, checkboxes on requirement lists. Once approved, it feels like progress (though often it’s not).

The worst part about buying AI SaaS is: it’s often just sitting there. It’s not integrated into actual workflows; it becomes just another login system. You’re forced to migrate data, and after a month, it’s just another vendor to manage. After 12 months, it’s abandoned, but you can’t get rid of it because switching costs are high—creating “technical debt.”

Building your own AI agents based on your existing systems avoids this problem.

They run within your current tools, don’t create new platforms, and actually make existing work faster. The agent handles tasks, humans only review results.

The real cost comparison isn’t “development cost vs. licensing fee,” but a simpler logic:

SaaS accumulates “technical debt”: each tool bought adds another integration to maintain, another system that will eventually become outdated, or a vendor that might be acquired, restructured, or shut down.

Self-built AI capabilities accumulate “ability”: each improvement makes the system smarter, each new workflow expands possibilities. Investment grows with compound interest, not depreciates over time.

That’s why I’ve been saying for the past year: general AI SaaS has no future. Industry data confirms: most companies that purchase AI SaaS stop using it within six months, with no productivity gains. Those truly benefiting from AI are companies with custom AI agents—either developed in-house or through third-party providers.

This is why early adopters of intelligent agents hold a long-term structural advantage: they are building increasingly powerful infrastructure. Others are just renting tools that will eventually be replaced. In a rapidly changing field, wasting a week can be a huge loss for your product roadmap and overall business.

Lesson Six: Deployment Must Be Fast

If your AI project takes a year to go live, you’ve already lost.

Plans can’t keep up with change. Your designed workflows may not match actual work methods, and edge cases you didn’t anticipate are often the most critical. In 12 months, the AI landscape could change dramatically, and your work might already be outdated.

You must get into production within 3 months.

In this era of information overload, real capability lies in: knowing how to effectively leverage information, and collaborating with it rather than fighting it. You need to do real work: handle actual tasks, make real decisions, and leave traceable records.

The most common problem I see is: internal development teams often estimate 6–12 months for an AI project that should take 3. This is not entirely their fault—the AI field is complex.

So you need engineers who truly understand AI: they know how AI scales, have seen real-world problems, and understand AI’s capabilities and limits. Too many “half-baked” developers think AI can do everything—that’s far from reality. If you want to enter enterprise AI, you must solidly grasp its actual capabilities and boundaries.

Summary

Building usable agents boils down to these points:

Context is everything: an agent without good context is just an expensive random number generator. Ensure smooth information flow, persistent memory, and embedded domain knowledge. The “prompt engineer” joke is outdated; now it’s “context engineer” 2.0.

Design for “augmentation,” not “replacement”: let humans do what they’re good at, and clear the path for them, making them more focused.

Architecture trumps model choice: whether to use independent, parallel, or collaborative agents—this decision is far more critical than which model to pick. Get the architecture right first.

Intercept and resolve issues proactively, not just report and review: dashboards are the “graveyard” of problems. Build systems that force issues to be quickly resolved.

Deploy rapidly and iterate continuously: the best agents are those already in production and constantly improving, not still in design. (And keep your schedule tight.)

Everything else is detail.

Technology is ready, but you might not be prepared.

Understanding this will enable you to scale your business 100x.