AI agents that don’t loop forever
I build and harden tool-calling agent systems with production rules: bounded execution, safe retries, audit trails, and recovery paths. No magic prompts. Just control.
Budget caps, step limits, and stop rules so the agent can’t spiral into runaway cost or unsafe actions.
Traces and decision logs that tell you what tool failed and why the agent chose an action.
Built-in fallback paths, retriable error categories, and runbooks so humans can step in calmly.
The problem
Most agents fail in the same boring ways: they loop, they retry unsafe actions, and they generate “explanations” instead of reliable execution. The fix isn’t more prompt tweaking — it’s control and observability.
- Infinite loops and runaway tool retries
- Unsafe side effects (double-clicks, duplicates, spam)
- No traceability: “it failed” with no decision history
Outcomes
You should be able to answer: what happened, what was retried, and what was prevented — without guessing.
- Bounded execution (budgets, timeouts, step caps)
- Safe side effects via idempotency + dedupe
- Debuggable traces and decision logs
How it works
For hands-on delivery, start with services. For reusable assets, join Axiom Ops.
Pricing (typical)
Pricing depends on how complex the tools are and how much needs to be hardened (budgets, traces, retries, and safe side effects).