There’s a lot of hype around AI agents right now. Autonomous systems that can research, decide, and act — all on their own. The vision is compelling. The reality is messier.
We’ve built a lot of agents at this point. Some worked great out of the gate. Others took painful iteration. Here’s what we’ve learned about the difference.
Start with one job
The biggest mistake we see is scoping an agent to do everything. “It should handle inbound leads, respond to support tickets, update the CRM, and generate reports.”
That’s not an agent — that’s a department.
The agents that work well do one thing reliably. A research agent that gathers prospect data. A triage agent that classifies incoming tickets. A drafting agent that writes first-pass responses for review.
Start narrow. Prove it works. Then expand.
Design for failure
Agents will make mistakes. The question isn’t whether — it’s what happens when they do.
Every agent we ship has:
- Confidence thresholds — below a certain score, it escalates to a human
- Audit trails — every decision is logged with the reasoning
- Fallback paths — if an API is down or data is missing, the agent degrades gracefully
This isn’t extra work. It’s the work. Without it, you have a demo, not a product.
Keep humans in the loop (at first)
We almost always start agents in a “copilot” mode — they draft, suggest, or prepare, but a human approves before anything goes out. This does two things:
- Builds trust with the team that will use it
- Generates training data for improving the agent over time
Once accuracy is consistently high and the team is comfortable, we gradually increase autonomy.
Pick the right model for the job
Not every task needs the most expensive model. We regularly use smaller, faster models for classification and routing, and reserve larger models for tasks that need deeper reasoning.
The cost difference matters at scale. An agent processing thousands of items per day on GPT-4 costs 10-50x more than one using a well-tuned smaller model.
Measure what matters
“It feels like it’s working” isn’t good enough. We define success metrics before building:
- Accuracy — how often does the agent get it right?
- Time saved — how much human effort is actually reduced?
- Error rate — how often does it fail, and how badly?
- Cost per task — what does each agent action cost?
If you can’t measure it, you can’t improve it.
The bottom line
AI agents aren’t magic. They’re software — and like all software, they work best when they’re well-scoped, well-tested, and built with clear success criteria.
If you’re thinking about building an agent for your team, book a call and we’ll help you figure out where to start.