Building reliable AI agents with LangChain, LangGraph & AgentField

“Agent” has become a magic word. In practice, the agents that survive contact with real users aren't clever prompts — they're well-engineered systems with explicit state, evaluation and guardrails. Here's how we build them.

Treat the agent as a state machine

A prompt chain that calls a model in a loop until it 'seems done' is impossible to reason about. We model the task as an explicit graph of states and transitions using LangGraph: each node does one thing, edges encode the rules, and you can see exactly where a run is and why.

• States are explicit, so failures are localised and debuggable.
• Transitions are rules you control, not vibes the model improvises.
• Long-running tasks can pause, wait for input, and resume.

Use the right tool for the shape of the problem

1. LangChain for the building blocks — model calls, tools, retrieval and memory.
2. LangGraph for stateful, branching, multi-step workflows that need control flow.
3. CrewAI / AgentField for multi-agent systems where role-based agents collaborate and check each other.
4. LangSmith for tracing every run and evaluating quality as you iterate.

Evaluation is the difference between a demo and a product

If you can't measure whether a change made the agent better or worse, you're guessing. We build an evaluation set early — real inputs with expected outcomes — and run it in LangSmith on every change. Quality becomes a number that has to go up, not a feeling.

You wouldn't ship a backend without tests. Don't ship an agent without evals.

Guardrails and human-in-the-loop

Autonomy is a dial, not a switch. For actions that cost money, send messages or change data, we put a human in the loop or hard constraints around the tool. Outputs are validated against schemas, tools have permission checks, and the agent can't do anything the current user couldn't do themselves.

Observability and cost control

Every step is traced, costed and replayable through LangSmith and our AI Platform's metrics. When something goes wrong in production, we can replay the exact run; when costs creep, we can see which step is responsible and route it to a cheaper model.

Our checklist for a production agent

• Explicit graph of states — no open-ended loops.
• An evaluation set that gates every change.
• Schema-validated outputs and permission-checked tools.
• Human-in-the-loop on consequential actions.
• Tracing, cost metrics and alerting from day one.

The takeaway

Agents are reliable when you treat them like the software they are. The tools — LangChain, LangGraph, CrewAI, AgentField and LangSmith — are excellent; the discipline of state, evaluation and guardrails is what turns them into something you can put in front of a customer.