We Build AI Agents for a Living. Here’s the Part Nobody Tells You.

The Demo Is Easy. Production Is Not.

Building an AI agent that looks good in a demo is straightforward. Building one that works reliably inside an existing business is a very different problem.

The biggest challenge is rarely the AI model itself. The real challenge is everything around it.

Most companies already run on complex, fragile systems — some modern, some ancient, many undocumented. We’ve worked with organizations where critical workflows depend on tools that haven’t been updated in decades. In one case, an agent had to interact with software running on an operating system that should have been retired years ago.

Smaller companies aren’t immune either. We’ve seen customer data split across spreadsheets, inboxes, and ad‑hoc tools — all slightly out of sync. Before an AI agent can deliver answers, the data itself needs to make sense.

No one budgets for this part. But this is where most of the real work lives.

The AI Is Powerful — and That’s the Problem

Modern models are incredibly capable. They’re also incredibly confident.

Without guardrails, an AI agent won’t tell you it doesn’t know something. It will guess — and sound convincing while doing it.

We’ve seen this firsthand. A client wanted an agent to help with basic support requests. Early on, it encountered a question outside its knowledge. Instead of escalating, it fabricated an answer. That single response created more cleanup work than the automation saved.

The fix wasn’t more intelligence. It was more discipline.

We added:

• Clear boundaries for what the agent is allowed to answer
• Rules for when to stop and escalate to a human
• Full logs for every decision the agent made

The agent became less flashy — and far more useful. In production, boring beats clever every time.

Start Smaller Than You Think You Should

Most teams want to automate everything immediately. Sales. Support. Ops. Reporting.

That’s almost always a mistake.

The AI agent projects that succeed are the ones that start painfully small. One example: instead of automating an entire insurance workflow, we started with a single task — checking whether incoming forms were filled out correctly.

That’s it.

It saved a few hours per week. It wasn’t impressive. But it worked. And because it worked consistently, it earned trust. That trust made the next step possible.

You don’t start with autonomy. You earn it.

Your Data Matters More Than Your Prompts

AI agents don’t create clarity. They amplify whatever already exists.

If your data is clean, structured, and accessible, the agent will give you useful answers. If it’s messy, incomplete, or contradictory, the agent will simply surface that chaos faster.

A surprising amount of our work involves cleaning data, defining sources of truth, and deciding what not to expose to the agent. This isn’t glamorous. But it’s essential.

Costs Are Real — and They Add Up Fast

Every reasoning step an agent takes has a cost. Every retry. Every unnecessary message. Every overly verbose response.

We’ve seen AI bills spike overnight because an agent was allowed to “think out loud” too often. Production agents need:

• Cost controls
• Usage limits
• Sensible defaults

Otherwise, experimentation quietly turns into an expensive surprise.

The Honest Take

AI agents today are not autonomous employees. They’re highly capable, very fast, and surprisingly needy assistants.

They require supervision, iteration, and continuous improvement. When teams accept that reality, incredible things are possible. When they don’t, projects stall — or fail.

Our Approach at Solardevs

We don’t promise magic. We help teams:

• Start with the right problem
• Connect the right data
• Build agents with clear boundaries
• Deploy safely to production
• Improve relentlessly

That’s how you turn data into answers — and answers into real business value.