Trace, evaluate, simulate, and guardrail agent releases with Future AGI
Use Future AGI to instrument agent runs, run evals and simulations, apply guardrails, and turn production traces into release-quality feedback.
npx skills add agentskillexchange/skills --skill trace-evaluate-simulate-and-guardrail-agent-releases-with-future-agi
Use Future AGI when an agent team needs one reviewable reliability loop before and after release: tracing live calls, evaluating outputs, simulating edge cases, applying guardrails, and routing feedback into the next agent version. The operator connects an existing Python or TypeScript agent through the upstream instrumentation packages, records traces, builds datasets or simulations from representative failures, runs evaluators, reviews guardrail outcomes, and uses the dashboard or self-hosted stack to compare versions before rollout. Inputs are the agent application, instrumentation package, project name, datasets, evaluator configuration, simulation cases, guardrail policies, and optional gateway routing rules. Outputs are traces, eval scores, simulation findings, guardrail decisions, regression signals, and release notes for what changed. The scope boundary is production agent reliability review, not a generic observability platform, eval library, gateway, SaaS product, or framework listing.
What this skill actually does
Inputs and prerequisites: Future AGI Cloud or self-hosted stack, ai-evaluation Python package or @traceai/fi-core TypeScript package, OpenTelemetry-compatible instrumentation, datasets, evaluators, simulations, guardrail policies, and optional gateway.
Setup notes: Use Future AGI Cloud or clone the future-agi/future-agi repository and run the official self-hosted installer. For instrumentation, install ai-evaluation for Python workflows or @traceai/fi-core for TypeScript workflows, register a project, instrument the agent, then run traces, evals, simulations, and guardrails against the release candidate.
Source and verification boundary: use https://docs.futureagi.com as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.