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Pecta is the quality and reputation layer for AI agents. It sits between any AI agent and the tools it calls, running a configurable set of pass/fail gates over every output before anything reaches the user. Gates execute in-process on your infrastructure — no network round-trip on the hot path — and results ship asynchronously to the Pecta cloud to feed dashboards and a portable reputation score for each agent. Observability tells you what happened; Pecta prevents bad output from happening.

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Three integration modes

Choose the mode that matches your stack. You can mix modes across different agents in the same organisation.
ModeUse caseLatencyInstall
In-process SDKRTB, latency-critical Node.js pipelinesunder 15msnpm install @pecta/core
MCP proxy CLIMCP servers in Claude Desktop or Cursorunder 50msnpx pecta-proxy <server-cmd>
REST APIPython, Go, batch analysis, any HTTP client50–100msPOST https://api.pecta.ai/v1/evaluate

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Key features

  • Quality gates — parallel, fail-fast checks that run on the very first request with no training period. Built-in gates cover latency, schema validation, filesystem safety, PII detection, content signals, and a full RTB / OpenRTB suite.
  • Reputation scores — a portable 0–1000 score per agent_id, stored centrally and computed over a rolling window of the last 500 evaluations.
  • Privacy by architecture — Pecta never stores bid payloads, MCP tool inputs, or user content. Only metadata (gate name, pass/fail, reason, latency, timestamp) leaves your process.
  • Zero hot-path overhead — telemetry batches asynchronously; gates never block waiting for a network response.

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Get started

Pick the quickstart for your integration mode.

SDK quickstart

Install @pecta/core and gate your first agent output in Node.js.

Proxy quickstart

Wrap any MCP server with pecta-proxy — no code changes required.

REST API quickstart

Evaluate agent output over HTTP from Python, Go, or any language.