Designing AI agent workflows humans can trust.

The problem with most agentic systems

Most agentic workflows fail not because the AI isn't capable, but because the system around it doesn't support the AI's limitations. When you can't see what the agent did, when you can't audit why it made a decision, when there's no fallback path when the model is unsure, you end up with a system that works in demos and fails in production.

The pattern we use

We structure agentic workflows around a simple principle: the agent handles the routine path, humans decide the exceptions. This means:

Deterministic logic where AI judgement would be unsafe

Not everything needs an LLM. Validation rules, business logic that doesn't change, routing based on explicit criteria, these are all better served by deterministic code. We use AI where it adds value (classification, extraction, summarisation) and code where it adds reliability.

Structured outputs rather than free-text

When we ask an LLM to do something, we ask for structured output that we can validate. JSON schemas, typed responses, explicit fields. This makes the output predictable and the downstream processing reliable.

Observability over every prompt and tool call

Every interaction with the model is logged. Prompts, responses, latency, token counts, the tools the agent chose to use, the parameters it passed. When something goes wrong, you can see exactly what happened.

Evaluation harnesses that don't rely on vibes

We build evaluation suites that test the agent against known inputs and expected outputs. When we change the prompt, the model, or the surrounding logic, we can see exactly what changed in the output.

Human approval gates on consequential actions

For actions that matter (sending an email, updating a record, making a decision that affects a customer), we require human approval. The agent queues the action, a human reviews it, and only then does it execute.

Clear fallback paths when the model is unsure

We build confidence thresholds into every decision. When the model isn't sure, the workflow routes to a human rather than guessing. This means the system degrades gracefully rather than failing silently.

Why this matters

The difference between an AI feature that ships and one that gets turned off three months later is trust. Users need to trust the output. Operators need to trust they can debug it. Compliance needs to trust they can audit it. This pattern builds that trust.