AI Agents: What Nobody Tells You Before Production

The Demo Is Easy. Production Isn't.

Building an AI agent that looks good in a demo is straightforward. Building one that works reliably inside a real business is another story.

The biggest challenge is rarely the model itself. It's everything around it.

Most companies already run on complex, fragile systems—some modern, some legacy, many undocumented. We've worked with organizations where critical flows depend on tools that haven't been updated in decades. In one case, an agent had to interact with software on an OS that should have been retired.

Nobody budgets for this part. But it's where most of the real work is.

AI Is Powerful — And That's the Problem

Current models are very capable. They're also overconfident. Without guardrails, an agent won't say "I don't know"—it will make something up, and sound convincing.

We added: clear boundaries on what the agent can answer, rules for when to stop and escalate to a human, and full logging of every decision. The agent became less flashy and much more useful. In production, boring beats clever every time.

Start Smaller Than You Think

Almost everyone wants to automate everything at once. That's almost always a mistake. Projects that work start with something very small—e.g. one task: "check if incoming forms are filled correctly." Only that. It saved a few hours a week. It wasn't impressive. But it worked, built trust, and enabled the next step. You don't start with autonomy. You earn it.

Your Data Matters More Than Your Prompts

Agents don't create clarity; they amplify what exists. Clean, structured, accessible data → useful answers. Messy, incomplete or contradictory data → chaos, faster. Much of our work is cleaning data, defining sources of truth and deciding what not to expose to the agent.

Costs Are Real — And Add Up Fast

Every reasoning step, retry and unnecessary message has a cost. We've seen AI bills spike overnight because the agent could "think out loud" too much. Production agents need cost controls, usage limits and sensible defaults.

The Honest Conclusion

Today's AI agents aren't autonomous employees. They're very capable, fast assistants that need plenty of support—supervision, iteration and continuous improvement. When teams accept that, they can achieve great things. When they don't, projects stall or fail.

Our approach at SolarDevs: We help teams start with the right problem, connect the right data, build agents with clear limits, deploy safely to production, and improve continuously.