Building Production AI Agents: Tools, Stop Conditions, and Human-in-the-Loop
A demo agent loops a model and a few tools. A production agent survives malformed arguments, runaway loops, and an audit. Here is what separates the two.
Building an AI agent that works in a demo takes an afternoon: loop a language model, give it a couple of tools, watch it complete a task. Building one that survives contact with production — malformed tool arguments, infinite loops, irreversible actions, and a compliance audit — is a different discipline. This piece is about the gap between the two and the three things that close it.
What an agent is, precisely
An AI agent is a workflow that loops: the model proposes a tool call, the tool runs, the result feeds back into the model, the model proposes the next step, and the loop terminates on a stop condition. That is the whole pattern. RAG answers one question; an agent completes a multi-step task that depends on external systems — pulling a record, checking eligibility, drafting a response, filing an outcome. The power and the danger both come from the loop.
1. A typed tool catalogue
The first thing production agents need that demos skip is a typed tool interface. The model should not be able to call a tool with malformed or out-of-range arguments, because in production that is not a funny failure — it is a corrupted record or a charge to the wrong account. Each tool gets a strict schema, arguments are validated before execution, and validation failures feed back into the loop as recoverable errors rather than crashing the run. The model proposes; the runtime disposes.
2. Stop conditions that do not trust the model
A production agent needs a stop condition that does not depend on the model deciding it is finished. That means a hard step budget (the loop terminates after N iterations regardless), and a human-in-the-loop gate on any irreversible or high-impact action — sending money, deleting data, emailing a customer. The model can draft the action; a human approves the commit. Autonomy is granted per action class, not globally.
This is the single most common omission in agent projects that fail review. A model told to "keep going until the task is done" will, on a bad day, keep going. The step budget and the approval gate are the difference between a tool and a liability.
3. Replayability and the audit trail
Every agent run should persist the full trace of (prompt, tool call, arguments, result) tuples. This is not optional logging — it is what lets you reproduce a failure, debug a regression, and answer an audit query months later. In a regulated environment you will eventually be asked: on this date, for this customer, what did the agent do and why? Without a persisted trace, there is no answer. With one, the answer is a query.
The stack we use
For delivery we standardise on LangGraph for the orchestration graph and Composio for the tool integrations, with full execution traces persisted for replay and audit. LangGraph gives explicit control over the loop and its termination; tool integrations are typed and validated; and the trace store doubles as the audit log. The same architecture underlies our future Agent Builder product, where these patterns become a visual canvas.
When not to build an agent
Agents are the right pattern when work is genuinely multi-step and crosses several systems. They are the wrong pattern when a single retrieval answers the question — that is a RAG job, and the tradeoffs are in the RAG vs fine-tuning piece. Reaching for an agent when RAG suffices buys you a loop you have to govern for no extra capability.
How to start
The fastest route to a production agent is a fixed-scope engagement that ships one real workflow end to end — typed tools, step budget, approval gates, and a persisted trace from day one. That is exactly what AI Agent Implementation delivers, and every agent we build becomes a template in the platform. The product logic sits inside the four-pillars piece.