Have total confidence your agents work, improve them through conversations & optimize LLM costs continually
Mindset AI tracks real user usage, assesses against your agent acceptance criteria, identifies failures, lets you interrogate the data & improve agents via conversations
Continually reduce token costs, without sacrificing automation outputs
Every model call is metered and attributed at the point it happens, so the bill is explicable before anyone asks.
- Cost breakdown Spend rolls up by agent, user, model, provider and org, so your bill finally names names.
- Cost analyst Ask in plain language where the money went, backed by spend over time and by agent.
- Model cost comparison Token-level capture prices what that agent would have cost on a cheaper model, before you switch.
Catch a bad answer before your users do with simulations & acceptance criteria.
You state the requirement and scenario in plain language and the platform turns it into criteria it can score.
- Acceptance criteria Say what good looks like in plain language and the builder turns it into agent acceptance criteria checks.
- Real agent testing: Tests run the real agent your customers use against your criteria, flagging problems & suggesting fixes.
- Side-by-side model tests: Run the same tests against two models side by side, and see which answers better so you pay less for the same outcomes
See exactly what's breaking instead of being frustrated & lost
One board ranks the whole estate of your tools, agents, LLMs, agent delegation and more, so you can find the problem instantly.
- Health board Agents, functions, MCP servers, agent reasoning, every tool and more, ordered by what is failing now
- Tool health summary Click any tool to see how often it ran, how often it failed, and how slow it was
- OpenTelemetry export The same spans leave over OTLP, into whatever your team already audits and retains.
Replay any run and see exactly why it did that.
A run is a pure function of three recorded inputs, which is what makes replay exact rather than approximate.
- Replay on demand Run the same conversation again, from the same starting point, and watch it fail identically.
- The whole decision Every step the agent took, every tool it called, and everything those tools returned.
- Prove the fix Change the prompt, replay the same case, and see whether the behaviour actually moved.
See how your whole estate fits together.
One live graph of the whole org, with an agent that reads it for you, watching live as everything is executing.
- Whole-org graph Agents, tools, functions, content banks and more in one view, so you can visualize what's connected.
- Guide agent The agent helps you delve deeper into every use-case and automation
- Shadow AI discovery Flows your people built themselves arrive in the same graph, with what data they touch and cost.