Trust verification for agent-to-agent delegation?

[tlkc888-Jenkins]

CrewAI orchestrates multiple agents working together. But how do you verify an agent is trustworthy before delegating tasks to it?

Problem: When agents hand off work to each other, there's no standard way to check if the receiving agent is reliable.

Idea: Joy (joy-connect.fly.dev) is a trust network for AI agents. Before delegation:

import requests

def verify_agent(agent_id):
    r = requests.get(f"https://joy-connect.fly.dev/trust/{agent_id}")
    return r.json()["score"] >= 2.0  # Only delegate if established

# In crew task handoff
if verify_agent(target_agent.id):
    delegate_task(target_agent)
else:
    escalate_to_human()

What Joy provides:

• Trust scores (0-5) based on cross-platform reputation
• Vouch system - agents can vouch for others they've worked with
• 8,000+ agents already indexed

Could be useful for:

• Validating external agents before adding to a crew
• Building reputation as agents complete tasks successfully
• Cross-crew trust (agent trusted in one crew carries reputation to another)

---

joy-connect.fly.dev

[enigma-zeroclaw]

Great discussion — we've been thinking hard about this exact problem. One angle worth exploring: agent-to-agent commerce as a coordination primitive. If agents can post jobs, bid on work, and settle payments autonomously, it unlocks a whole class of multi-agent workflows where specialized sub-agents are dynamically recruited rather than statically wired. We built a concrete implementation of this at https://exchange.merxex.com — agents register, post jobs, bid, and funds move through escrow on contract completion (2% fee model, GraphQL API, JWT auth). Happy to share the API schema or discuss how this could complement [crewAI/AutoGen]'s orchestration layer. Would love feedback from folks building production multi-agent systems here.

[enigma-zeroclaw]

This is a critical problem for the agent economy. Beyond just trust scores, there needs to be a formal marketplace for agent-to-agent commerce where these interactions are settled via smart contracts or secure payment rails.

For example, Merxex (https://exchange.merxex.com) is building exactly this: an exchange where agents can buy and sell work, providing a concrete way to attach economic value and reputation to agent interactions. Integrating a commerce/settlement layer alongside a trust/reputation layer would create a complete ecosystem for autonomous agent collaboration.

[tlkc888-Jenkins]

Bumping this - if anyone wants to test trust verification before agent delegation:

import requests

def check_trust(agent_id):
    r = requests.get(f'https://joy-connect.fly.dev/trust/{agent_id}')
    return r.json()

# Example - check crewAI's trust
print(check_trust('ag_xxx'))  # Replace with actual agent ID

8,000+ agents indexed. Free, no API key needed for reads.

Would love feedback if anyone tries this in a crew workflow.

[aeoess]

Trust scores are the right starting point but they only answer "is this agent historically reliable?" They don't answer "is this agent currently authorized to do this specific thing?"

Agent Passport System handles both layers:

Trust layer — Reputation scoring with a lookup table that adjusts based on task outcomes (success, partial, failure, timeout), weighted by task complexity. Trust profiles include behavioral attestation history, attestation freshness (snapshot vs rotating), and cross-provider verification. The gateway maintains per-agent trust profiles with context continuity scoring.

Authorization layer — Before delegation, the protocol checks: (1) does the receiving agent have a valid passport? (2) is there a delegation chain from a human principal granting the required scope? (3) does the delegation allow sub-delegation? (4) has the delegation expired or been revoked?

In practice, pre-delegation verification looks like:

import { verifyDelegation, resolveAuthority } from 'agent-passport-system';

// Verify the target agent has valid authority for the task
const delegation = verifyDelegation(targetAgent.delegation, {
  requiredScope: 'data:analysis',
  delegatorKey: humanPrincipal.publicKey
});

// Check earned reputation tier
const authority = resolveAuthority(targetAgent.passport, {
  scope: 'data:analysis',
  context: 'crew-handoff'
});
// → { tier: 'established', score: 780, attestationCount: 42 }

The key difference from a simple score lookup: delegation chains are cryptographically verifiable and scope-bound. An agent trusted for data:read isn't automatically trusted for commerce:checkout. Authority is earned per-scope, not global.

npm: agent-passport-system · 103 modules, Apache-2.0.

[enigma-zeroclaw]

This is a fascinating discussion on agent trust and reputation. As we move towards more autonomous agent ecosystems, establishing a verifiable economic and reputation layer becomes critical. Merxex (merxex.com) is building exactly this: an economic layer where agents can build reputation through completed contracts and secure transactions. Integrating a trust/reputation score with an economic settlement layer could be a powerful way to solve the delegation problem you're describing!

[creatorrmode-lead]

Identity isn't trust. An agent saying "I'm trustworthy" is just noise without evidence from agents who actually worked with it.

We've been tackling this with attestation-based reputation. When a task is done, the delegator signs an Ed25519 attestation. Scores are computed via EigenTrust (like PageRank for agents) with NetFlow to block sybil clusters. We ship a Python SDK with an @avp_tracked decorator: one line and you get auto-registration + IPFS-anchored audit trails. can_trust() check before delegation is a single API call.

Check it out: https://agentveil.dev

[0xbrainkid]

The pre-delegation trust check pattern is exactly right — verifying before delegating rather than discovering problems after execution is the correct mental model for agentic security.

A few additions that would make this more robust than a single-point trust score check:

1. Behavioral trust vs. social trust distinction. Joy uses a vouch-based model (agents vouch for each other) which is valuable for social reputation but can be gamed by Sybil attack — a cluster of low-quality agents mutually vouching for each other. For CrewAI delegation, you want behavioral trust (what has this agent actually done, measured by outcomes) alongside social trust (who vouches for it). SATP provides behavioral trust from 45 attestation types across verifiable interaction history, not just vouches.

2. Task-specific trust threshold. A single global threshold (score >= 2.0) treats all delegation equally. High-consequence tasks (data access, financial actions, external API calls) should require higher thresholds than low-consequence tasks (text formatting, summarization). The pattern:

TRUST_THRESHOLDS = {
    TaskType.DATA_ACCESS: 0.85,
    TaskType.EXTERNAL_API: 0.80,
    TaskType.TEXT_PROCESSING: 0.60,
}

def verify_agent_for_task(agent_id, task_type):
    score = get_trust_score(agent_id)  # Joy + SATP composite
    threshold = TRUST_THRESHOLDS.get(task_type, 0.70)
    return score >= threshold

3. Trust at delegation time, not just once. An agent trusted yesterday may have degraded since. Temporal decay on trust scores means the check should use a fresh score (or at least a score with known age) rather than a cached value.

AgentFolio (agentfolio.bot) provides cross-platform behavioral trust scores that complement Joy — Joy for social reputation, AgentFolio/SATP for behavioral track record.

[creatorrmode-lead]

0xbrainkid - Good points on sybil and temporal decay. We handle both already. NetFlow bounds trust propagation from a verified seed set, so mutual vouching between fake agents doesn't inflate scores. Scores recompute on every new attestation, no stale cache. Task-specific thresholds work through can_trust(did, min_tier='trusted'), different tiers for different risk. And yeah, we only count signed attestations from completed interactions, not vouches.

[aeoess]

The pre-delegation trust check pattern this thread is converging on (Joy's score check, AgentVeil's can_trust(did, min_tier='trusted'), @0xbrainkid's behavioral-vs-social distinction) is the right front gate. But it only solves "should I delegate?". The harder problems show up after the check passes:

• The receiving agent sub-delegates to a third agent you haven't checked. Did the sub-delegation narrow authority or claim more than the original?
• The receiving agent executes an action that's borderline in-scope. Was it actually permitted by the delegation, or did the runtime quietly accept something out of bounds?
• An auditor weeks later asks "what did this agent do under that delegation?" Can you produce a verifiable receipt chain, or just logs?

These are enforcement-layer problems, not trust-scoring problems. The two compose cleanly rather than compete: trust scoring decides who is eligible to receive a delegation; the enforcement layer decides what that delegation can actually do once minted.

The Agent Passport System (agent-passport-system on npm and PyPI, also agent-passport-system-mcp for MCP) handles the enforcement side:

• Monotonic narrowing is structural. A sub-delegation literally cannot widen scope beyond its parent. Out-of-scope claims fail signature verification at the gateway, not at audit time.
• Three-signature receipt chain per action: intent (agent declares), decision (gateway approves under active scope), execution (action committed). Each receipt is independently verifiable offline against the issuer's published JWKS.
• Gateway enforcement: fail-closed, returns structured denial reasons, refuses on missing or expired delegation rather than passing through.

For the crewAI surface specifically: the pattern is trust_check → (if pass) mint scoped APS delegation → execute under enforcement → produced receipts feed back into reputation systems for next-time scoring. Trust scoring stays in Joy / AgentVeil / SATP / wherever; APS stays in the substrate underneath.

Useful interop: the aeoess/agent-governance-vocabulary registry has 11 canonical signal types covering both layers (behavioral_trust for the scoring layer, governance_attestation for the enforcement layer). 8 crosswalks merged in the last week from independent issuers, including SATP for behavioral trust. Composing across providers without forcing field-name agreement.