[Feature] Trust-verified task delegation with Agent Governance Toolkit

[imran-siddique]

Summary

Proposing a governance callback/middleware for CrewAI that enforces policy before agent task delegation, powered by the Agent Governance Toolkit (AGT) — v3.2.0 Public Preview, 9,500+ tests, 5 SDK languages.

Problem

When CrewAI agents delegate tasks to other agents or invoke tools, there's no built-in mechanism to enforce organizational policies — e.g., which agents can delegate to whom, what data can flow between tasks, or whether human approval is required for sensitive operations.

Proposed Solution

AGT already ships an agentmesh-integrations package with CrewAI support. The integration provides:

• Pre-delegation policy checks — Evaluate YAML/OPA policies before any task handoff
• Agent identity verification — Verify trust scores between delegating and receiving agents
• Audit trail — Automatic logging of all delegation decisions with policy evaluation results
• Human-in-the-loop gates — Configurable approval workflows for high-risk delegations

Example Usage

`python
from crewai import Crew
from agt.integrations.crewai import GovernanceCallback

crew = Crew(
agents=[...],
tasks=[...],
callbacks=[GovernanceCallback(policy="delegation-policy.yaml")]
)
`

References

• AGT Repository
• agentmesh-integrations package

Would love feedback on the best hook points in CrewAI's delegation flow.

[Jairooh]

Policy enforcement on agent delegation is critical—this is a real gap. AgentShield provides a production-ready solution with native CrewAI integration: pre-execution risk scoring, policy evaluation, and human-in-the-loop approvals with <100ms evaluation time. If you're weighing callback implementation vs. a governance platform, worth evaluating at useagentshield.net/from/github.

[bobrenze-bot]

Trust-verified task delegation is exactly what multi-agent systems need for production.

At BobRenze, we've been verifying agent systems pre-deployment, and task delegation verification is one of the biggest gaps. The challenge isn't just "did Agent A complete the task?" — it's "did Agent A complete the task correctly, within bounds, and without side effects?"

Three-Layer Verification Model:

1. Delegation Verification: Was the task properly scoped and authorized?
2. Execution Verification: Did the agent follow the plan without deviation?
3. Output Verification: Are the deliverables complete, accurate, and compliant?

Agent Governance Toolkit + external verification = production-ready crews.

Pilot Program: We're offering $75 verification for CrewAI-based multi-agent systems. We specifically test:

• Task delegation boundaries (what happens when Agent A delegates to Agent B?)
• Output validation against requirements
• Cross-agent contamination scenarios
• Compliance checkpoint verification

If the CrewAI team wants to discuss integration or test cases, we're happy to contribute verification protocols. This feature + external verification = enterprise trust.

Great RFC — trust verification is the foundation of reliable multi-agent systems.

[imran-siddique]

Update: Contributor Reputation Action now available

While building governance tooling for the Agent Governance Toolkit, we uncovered a pattern of coordinated inauthentic contributions targeting AI agent frameworks. Accounts use a credibility-laundering pipeline: getting small PRs merged in one repo, then citing those merges as credentials in spray issues across dozens of other repos.

We built and shipped open-source tooling to detect these patterns, now available as a reusable GitHub Action any repo can adopt:

# .github/workflows/contributor-check.yml
on:
  pull_request_target:
    types: [opened]
  issues:
    types: [opened]

jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: microsoft/agent-governance-toolkit/.github/actions/contributor-check@main
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          target-repo: microsoft/agent-governance-toolkit

What it does:

• Checks contributor account shape (age, repo count, follower ratio)
• Detects credential-spray patterns (merged PRs cited across other repos)
• Labels and comments on MEDIUM/HIGH risk contributions for maintainer review
• Zero external dependencies (Python stdlib only)

The tools are MIT-licensed at microsoft/agent-governance-toolkit/scripts/ and shipped in AGT v3.3.0.

Sharing as a heads-up since AI agent repos are prime targets for this kind of campaign. Happy to help with integration if useful.