How to Build Reliable AI Agents
AI agents get discussed as if the main challenge is prompting a model to 'think' through a task. In practice, the engineering problem that actually determines whether an agent is usable in production is much narrower and much less glamorous: how do you detect and contain a mistake before it compounds across several sequential steps. This is what that looks like in practice.
What an AI agent actually is
An agent is a system that uses a language model to decide a sequence of actions toward a goal, using tools it's been given, rather than producing a single response to a single prompt. The defining trait is a loop: observe the current state, reason about what to do next, act, observe the result, and decide again.
Why reliability is the hard part
Every additional step in that loop is a new place for an error to occur, and small per-step error rates compound fast across a multi-step task. A model that's correct 95% of the time at each individual step is only right about 60% of the time after ten sequential steps, if nothing along the way catches a mistake.
Insight · The real engineering problem
It's rarely 'how do I get the model to reason well.' It's 'how do I detect and contain a mistake before it compounds' — which is a systems and verification problem, not a prompting one.
Architecture
A reliable agent architecture includes a clearly scoped task definition, tool definitions with strict input/output schemas rather than free text, an explicit planning and reasoning loop, real state and memory rather than relying on the model to hold everything in context, and — the piece most systems skip — verification steps between actions, not only at the very end.
Implementation approach
Start with a narrow, well-defined task scope, give the agent only the tools it actually needs rather than everything available, make every tool call typed and validated instead of free-form, and log every step it takes. Broader autonomy is something an agent earns by proving reliable on a narrow scope first, not a starting point.
Common mistakes
- Granting an agent broad scope and autonomy before it's proven reliable on a narrow task.
- No verification step between actions, so errors chain without ever being caught.
- Free-text tool calls instead of structured, validated ones.
- No clear escalation path — either the agent asks a human for everything (useless) or never does (dangerous).
- Treating a successful demo as evidence of reliability without testing edge cases and adversarial inputs.
Production considerations
Multi-step reasoning has real cost and latency implications that compound with task complexity. Timeout and retry behavior needs to be explicit rather than assumed, and every action an agent takes should be observable and attributable after the fact — not just the final outcome.
Security & reliability
Agents that can take real-world actions — sending messages, modifying records, spending money — need explicit permission boundaries, and anything irreversible should require human confirmation rather than agent judgment alone. A useful mental model: an agent's tool access should be scoped as tightly as a new human employee's would be for the same task, not more broadly just because it's automated.
When to use agents
- Multi-step tasks with clear success criteria and tools already available to act with.
- Not tasks better solved by a single well-prompted call or by traditional rule-based automation.
Business use cases
- Routing and triage across systems that require judgment, not just fixed rules.
- Research and multi-source data-gathering tasks.
- Multi-step operational workflows — lookup, decide, act, notify — that currently need a person to drive every step.
Key takeaways
- Reliability comes from containing errors at each step, not from a smarter model alone.
- Scope agents narrowly first — broader autonomy is earned, not assumed.
- Every tool call should be structured and validated, never free text.
- Irreversible actions need explicit permission boundaries, not agent judgment.
Keep reading
