What is AgentOps?+
AgentOps is the engineering discipline of making AI agents reliable in production — the harness around a model: state checkpoints, idempotent tool calls, retry budgets, cost guards, evals, and observability. Bonfire treats it as the flagship of every build, where reliability is measured (tool-call success rate, retry rate, p95 loop latency, cost-per-output), not asserted.
How do you make an AI agent reliable in production?+
We architect the harness so failure is recoverable by construction: state is checkpointed before any side effect, every tool call carries an idempotency token, integrations sit behind a permissioned read-versus-write layer with circuit breakers and retry budgets, and a recovering agent resumes from the last good step instead of repeating work.
What is an idempotency token in an agent system?+
An idempotency token is a stable key — for example a hash of agent_id + step + payload — attached to a tool call so that if an agent retries after a side effect already landed (an email sent, a row written, a payment made), the system recognizes the duplicate and skips it. It is the fix for the most common silent failure in prompt-first agents.
Do you build custom MCP servers?+
Yes. We build Model Context Protocol (MCP) servers when the tool an agent needs does not exist. Our open-source reaper-mcp (58 tools, on PyPI) is public, installable proof; we also build custom MCP servers for agent-driven automated testing and ML-training in DAW and video-editor environments.
What does “the harness is the product, not the prompt” mean?+
The model is the easy part. The harness is everything that lets it work safely: tool definitions, permissions, memory, state, checkpoints, evals, recovery, and observability. That scaffolding — not the prompt — decides whether an AI system survives production. It is what separates an AI system from an AI feature.