The Data Plane for Production AI Agents.
Agent fleets are unpredictable — spend spikes overnight, loops burn budget, reliability drifts. AOT Labs is the data plane between your agents and every model provider — it runs in your environment and makes agent operations visible, predictable, and cheap to run at scale.
See
LiveFleet-wide visibility into where every dollar and second goes — by agent, model, and tool — with cost per successful task as the metric that ties spend to reliability.
Optimize
LiveThe engine finds and removes waste automatically — redundant context, dead tool output, model overuse, cache breaks — and tells you exactly what's safe to apply, and what it'll save.
Control
On the roadmapGuardrails that act before the spend happens: runaway-loop detection, per-team budget caps, and spend policy enforced across every agent in production.
Steered by the AOT control plane — budgets, routing policy, and fleet-wide spend visibility. Start today by pointing AOT at your run logs (no code change); graduate to the in-path data plane in your VPC when you're ready.
Repeated context
The same content re-sent across runs — pin it once instead of paying every time.
Unused tools
Tool schemas re-billed every turn that the agent never actually calls.
Stale & dead output
Tool output that's hauled for hundreds of turns but never referenced again.
Retry loops
Failed calls retried verbatim, and the same command run over and over.
Model overuse
Frontier models on mechanical steps a smaller model handles for a fraction of the cost.
Bloated output
Verbose logs and build noise that compress losslessly at admission, no cache penalty.
Cache breaks
A changed prefix that re-bills cached tokens at full price — we trace it to the step that caused it.
…and more
Context-window pressure, sub-agent overhead, and new classes as we learn them. See a sample audit →