AI agent platforms, skills and tools the agent can actually call, evals that catch the regressions.
Agent demos are easy. Agent products are not. We build platforms where the model has a typed tool layer, a skill catalog the operator can compose, an eval harness that runs on every change, and an observability path that shows exactly which tool the agent called and what came back. The boring infrastructure is the moat.
What we build
Typed tool catalog, not free-form function calling
Tools are typed at the boundary. The model is given a JSON-schema description; the runtime validates the call before execution. Bad tool invocations surface as typed errors the model can reason about, not as silent failures.
Skills as composable units, not monolithic prompts
A skill bundles a system prompt, a tool set, and a small eval suite. The operator composes skills into agent flows. Adding a skill is a unit change, not a rewrite of the master prompt.
Multi-model routing where the cost math works
Cheap models for high-volume narrow tasks (classification, extraction); frontier models where reasoning quality earns the price difference. The router is observable, operators see per-route cost + latency, not blended numbers.
Evals that ship with the agent
Each skill has a labelled eval set checked on every change. CI fails when an eval regresses. Production traffic samples back into the eval pool so the agent doesn't drift between releases.
Long-running runs handled by the queue
Agent runs that take minutes (or hours) run as background jobs. Progress streams to the UI; failures retry with backoff; humans can intervene at decision points. The request handler never holds an open connection waiting for a model.
Per-call observability with stack traces
Every model call, tool call, retry, and error gets a trace span. Operators can replay an agent run end-to-end, see exactly which decision branched where, and audit what the agent did on behalf of which user.
Where this fits
Your agent demo works in the founder's hands and falls over the moment a real user types something slightly off-script.
You're running an agent product and the model spend is unpredictable because there's no cost ceiling and no router.
Your team is shipping agent features and there's no eval discipline, every release feels like rolling dice.
Tech stack
- TypeScript
- Anthropic API
- OpenAI API
- BullMQ
- Postgres
Want this for your team?
30 minutes with a founder or senior engineer. We'll scope what you need and tell you straight whether Stacklane fits.
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