AI agent identity, signed credentials, delegated authority and portable governance evidence. Python core and offline verifier SDKs.

An autonomous software factory that knows what it is supposed to deliver, and proves it did.

Stop AI agents from doing things you didn't ask for.

The practices that made software work didn't stop working. They stopped keeping up. PAAD brings them back at AI speed.

Rules and checks that make Claude Code look around a change, not just at the lines it writes: ten questions before code, a reviewer that didn't write it, proof before done, bugs fixed as a class.

Enables capturing a tamper-evident, hash-chained local record of every AI agent tool call — both the decision before a tool runs and its outcome — so you can review what the agent actually did in order, detect edits, deletions, or reordering at the exact index, and export a signed evidence pack any third party can verify with no installs.

Signed, publicly logged records of what your AI agent did, with proofs anyone can check offline.

Search ClinicalTrials.gov trials, retrieve study details and results, and match patients to candidate trials via MCP. STDIO or Streamable HTTP.

Use after a failure, user correction, repeated workflow problem, or validated success reveals a reusable lesson. Captures bounded redacted candidates, runs executable behavior evals, and separates validation from application in durable guidance.

DSH Plugin Radar — open-source ecosystem radar for DeepSeek Harness plugins: continuous discovery (21k+ candidates), k8s runtime validation (13k+ tests), 15-min snapshots; the catalog is a generated artifact — 开源 DSH 插件生态雷达：持续发现 2.1 万+ 候选、k8s 运行级实测 1.3 万+、15 分钟快照；插件目录为自动生成的产物

Stores what AI agents learn — thoughts, decisions, session events and repo facts — in PostgreSQL with pgvector and serves it back through versioned MCP tools with hybrid vector-plus-full-text retrieval and budgeted context packs. Enables multiple agents to share one scoped, role-isolated long-term memory, with deliberate promotion of distilled candidates into durable memories.

Enables agents to analyze OpenAPI contracts for deterministic integration risks, plan auth, sync, action, and webhook candidates, and diff contract versions for likely breaking changes through read-only tools.

Audit trail and traceability for AI agents. See what every coding agent did, block risky calls, ask a human for approval, and prove it with signed trust checks.

See what a conversation feeds the model, what your rules keep out, and what one click would change.

Enables agents to perform autonomous multi-angle specular glare healing and planar foil defect metrology by ranking candidate views, fusing glare-free views, and finalizing inspection or escalating unresolved cases through MCP tools.

Exposes three read-only tools that list local Agent and MCP runtime candidates, inspect running processes, and check a live runtime against an approved artifact expectation. Returns explicit verdicts such as matched, stale, leaked, conflict, unknown, or not running without exposing argv, environment values, or file contents.

Signed, publicly logged records of what your AI agent did, with proofs anyone can check offline.

Check an AI agent you did not build: is it who it claims, inside its mandate, and what's its record