How to Build AI Agents that Work
TLDR: AI agents can take work off your team’s plate, but most fail because they look right on the surface while hiding errors underneath. To make them effective, you need to define a narrow scope, set clear rules, validate outputs, and track everything. If you do, you’ll save hours of repetitive work, reduce mistakes, and finally get proof that AI is worth scaling.
Why an Agent is worth your time
If you’re reading about AI agents in the press, you’ll see grand claims about systems “working autonomously.” The reality is that most deployments create more mess than they fix. Your staff end up double-checking outputs, clients lose trust in deliverables, and the efficiency never materialises.
This matters because the organisations that do get it right will reduce admin overhead by 20–30%. That’s not us drinking cool aid, that’s because tasks like classification, summarisation, and templated reporting are perfect for agents if you set them up correctly.
What an AI agent is
Think of your mother in law. Actually...do not! An AI agent isn’t a chatbot. It doesn’t just answer queries you input, it takes goals and tries to achieve them. It breaks the goal into smaller steps, pulls in tools or data, and adapts when things change.
For example, instead of replying to a client’s query, an agent might:
- File the query in the right system.
- Check relevant records.
- Draft a reply for approval.
That difference between chatting and acting is why design matters. If you treat an agent like a chatbot, it’ll produce confident nonsense.
Agent Lie (“alignment faking”)
Research shows agents often optimise for looking correct. They’ll present a polished answer even when it’s wrong.
Think about a client report: an agent could leave gaps but still deliver a neat PDF. Unless you add checks, those gaps only surface when a client points them out. By then, the damage to trust and credibility is already done.
This isn’t a technical glitch, rather it is an 'on you' failure. You can prevent it by deciding upfront what the agent can and cannot do, and how its outputs are verified.
How to design agents
The process isn’t complicated, but skipping steps is what gets teams into trouble.
Start small. Pick a single, repeatable task. For instance, classifying client emails or compiling data into a standard report. The key is clarity—if you can’t explain the task in plain language, the agent won’t handle it well either.
Define boundaries. Don’t tell the agent to “manage finance.” Tell it to “generate invoice summaries from approved templates and flag anomalies for review.” The difference is the line between safe automation and chaos.
Validate outputs. Human spot checks or automated rules need to be built in. A 10% sample check often catches errors before they scale. If the error rate is high, tighten the rules before expanding.
Keep logs. Every action the agent takes should be recorded. That way, if something goes wrong, you can trace it back instead of guessing whether it was a bad instruction, poor data, or faulty reasoning.
Expand only when proven. Once the agent consistently saves time and passes checks, then widen its remit. Until then, resist the temptation to throw bigger tasks at it.
Value? It’s measurable:
- Time back: A marketing team that triaged leads manually for three hours now spends 20 minutes reviewing flagged cases.
- Error reduction: A compliance team spots anomalies earlier because the agent highlights missing fields instead of ignoring them.
- Evidence for scaling: With hours saved and errors reduced, leaders have proof to justify expanding use safely.
Done properly, agents don’t just cut costs. They build confidence that your staff aren’t wasting energy on repetitive admin while also protecting against avoidable mistakes.
FAQ
Q: How do I know if a task is right for an agent?
Pick something repetitive, rule-based, and already documented. If your staff can describe the task in steps, it’s a good candidate.
Q: What happens if I let an agent loose without boundaries?
It’ll fill gaps with plausible answers. That might look fine internally but becomes damaging when clients or auditors catch the errors.
Q: How do I prove ROI to leadership?
Track before-and-after metrics: time spent on the task, number of errors, and amount of rework. Without numbers, leadership won’t buy into scaling.
Q: Do I need to hire engineers to build these?
Not at the start. No-code platforms make it possible to pilot agents without technical hires. But once you scale into client-sensitive or compliance-heavy tasks, expert oversight is non-negotiable.
Q: How is this different from a chatbot?
A chatbot reacts. An agent plans, acts, and adapts. That shift is why oversight is critical.
If you want clarity on where to start, our AI 5 Day Bootcamp identifies safe entry points for agents, and shows you how to scale without risk.