Custom AI Agents
Agents scoped to one job and held to it, with tool access, guardrails, and a full trace of every decision they made.
- TriggerGoal received
- ClaudePlan steps
- ToolsQuery systems
- EvalCheck own work
- GateHuman approval
- OutcomeAction taken
The agent plans, calls the tools it needs, checks its own work, and stops for a human before anything irreversible happens.
Narrow beats general, every time
The agents that work in production are boring. They do one job. They have access to four tools, not forty. Their instructions fit on a page, and their success criteria are written down before anyone builds anything.
The agents that fail are the ones asked to be an assistant in general. Given a vague brief and broad access, they wander, and nobody can say afterwards whether the output was right. We do not build those.
Everything is inspectable
Every run produces a trace: what the agent was asked, what it retrieved, which tools it called with which arguments, what came back, and how it reached the output. When someone asks why it did that, the answer is a link, not a shrug.
Traces are also how the system improves. Failures become test cases, and the test cases stop the same failure returning after the next change.
Where the human stays
Anything that leaves your organisation or cannot be undone waits for approval. Sending client-facing email, moving money, changing a record of truth: the agent prepares it, a person releases it.
In practice this is not friction. Reviewing a well-prepared draft takes seconds, and it is what makes teams willing to let the system near real work at all.