Your First AI Agent Should Do One Thing Badly

Our most effective agentic systems all started embarrassingly simple. Fewer agents, narrow tasks, doing it badly 😂.

There is a reason for this, which highlights a key difference between traditional software development lifecycles and Agentic Systems development lifecycles.

Agentic systems turn out to be much better when you build them iteratively. Crawl, walk, run. And the teams that figured this out and embraced it fully are running in production while everyone else is still polishing their demo.

The POC Graveyard

This is a common scenario: a team of skilled engineers decides to build an agent. They are proficient in multi-agent collaboration, chain-of-thought reasoning, tool orchestration, prompting engineering and everything in between.

They design what they believe is the ideal system from the start, think about all agents out of the gate, for example: a researcher agent provides input to a planner agent, which coordinates with an executor agent and validates results through a quality checker. The architecture has been well-documented, with detailed diagrams, sophisticated handoffs, and some error handling.

Fast forward three months, and the project is still stuck in development. Great README, but zero production traffic (therefore 0 ROI 🤷).

I don't think the problem is ambition, but instead it's that they're optimizing for a system they don't fully understand yet.

The same thing that makes these systems super powerful, the ability to orchestrate intelligence and point it into complex problems, is the thing that makes them different from other system we have built before, and that implies how building it in a different fashion to some extent.

Starting Simple in High-Stakes Domains

A healthcare staffing company approached us with an interesting use case around credentialing that grew into a major bottleneck, every time they onboard a physician, they must verify licenses, query medical boards, check sanction lists, and validate identity. This manual process, involving multiple data sources, often takes days to complete. On top of that, in healthcare staffing, delays in clearing a doctor can have consequences beyond lost revenue.

Instead of starting by automating the entire onboarding pipeline, they picked one thing: the background check workflow.

The initial version was straightforward, collect provider data, query relevant sources, structure the output as JSON, make decisions based on the data, and push results to Snowflake. Not every edge case was addressed.

They could have spent months anticipating every scenario, but instead delivered a solution in weeks. After battle testing, they now get to implement many other quality levers to balance load, incorporate human review, and run parallel workflows, all of which enable learning from real executions rather than extended planning sessions.

Now, they have implemented guardrails for hallucination detection, audit trails for compliance, and a process that completes in hours rather than days. A second use case is already in development.

If they had implemented all checks and balances from the get go they would probably miss much of the opportunity to make the actual use case and architecture better, similar to adding caching before addressing performance issues in traditional applications.

If I Were Starting Today

Narrow scoped agents, fewer tasks, this week not this quarter. The goal isn't to build something impressive, it's to build something you can ship fast enough to learn from. If it takes longer than a few weeks, you're probably building too much. Cost of iteration is too low for you to not take advantage of that.

Human in the loop is a feature, not a limitation. Start with human review on as much as you are comfortable if needed, this is how you build the feedback loop that makes everything else better. The humans reviewing outputs will tell you what's actually wrong, not what you imagined would be wrong, then you dial it down as the system earns trust, 100% becomes 80% becomes 50%, and eventually autonomous.

Make failure satisfyingly obvious. Don't build sophisticated error recovery yet when testing, build obvious error surfacing, you want to see failures clearly so you can understand them. In traditional software you might want graceful degradation, in agentic systems you want loud failures early on development and testing so you can fix the actual root cause.

Weekly iteration vs quarterly roadmaps. Look at what actually failed last week then sharpen the prompts, fix the vague parts, add guardrails, focus on where real problems showed up and leverage all control levers you have.

Add agents when you have evidence, not intuition. "I think we need a validator" is a guess, better off being able to rattle off things like "47% of errors are formatting issues a validator would catch" Multi-agent architectures can be powerful but they also multiply the debugging surface area, so think about these systems progression as you earning your way into that complexity by proving you need it first.

The thing that makes agentic systems powerful, the ability to orchestrate intelligence and point it at ambiguous problems, is the same thing that makes them different beast to build, you can't fully spec the behavior upfront because the behavior emerges from the interaction between prompts, tools, data, MCPs and the model itself. The only way to understand that interaction is to run it against real inputs and watch what happens.

That's not a limitation of the technology. That's just how it works. And once you embrace that, shipping becomes a lot more straightforward.

Why This Feels Wrong (And Why It Works)

I get why this approach may feel counterintuitive.Engineers are trained to try to anticipate edge cases, and design for scale from day one, so even considering shipping something that you didn’t polish all the pixels to be as shiny as it can feels odd.

But here's what we keep seeing: teams stuck in POC purgatory aren't less capable, the problem is they're optimizing for a system they don't yet understand, they're writing error handling for errors that won't happen while missing the failures that will, and sometimes building for scale before they know what's worth scaling.

Your first agent should do one thing badly.

That's probably the path to the sophisticated system you're imagining, just not in the order you expected.