How to Implement Agent-to-Agent Delegation in Python: A Complete Guide to the Trust Layer

Agent-to-agent delegation enables autonomous LLM agents to securely assign tasks to other agents using scoped permissions, trust scoring, and cryptographic signatures as implemented in the awesome-llm-apps trust layer.

The awesome-llm-apps repository provides a production-ready framework for implementing agent-to-agent delegation through its multi-agent trust layer. This system ensures that when one autonomous agent delegates work to another, every action remains constrained by explicit permissions, auditable through immutable logs, and governed by dynamic trust scores that adjust based on behavior.

Core Architecture for Agent-to-Agent Delegation

The trust layer in advanced_ai_agents/multi_agent_apps/multi_agent_trust_layer/multi_agent_trust_layer.py implements delegation through seven interconnected components that enforce security at every step.

Identity Registration and Trust Scoring

Before any agent-to-agent delegation can occur, agents must register with verified identities tied to human sponsors. The IdentityRegistry stores these credentials, while the TrustScoringEngine maintains a 0-1000 trust score per agent.

When an orchestrator registers via TrustLayer.register_agent(), the system creates an AgentIdentity and initializes a TrustScore object. High initial scores (800-1000) indicate privileged agents capable of creating delegations, while lower scores restrict agents to receiving delegated tasks only.

Delegation Scope and Policy Enforcement

Every delegation operates within a DelegationScope that explicitly defines:

• Allowed actions: Specific operations the delegate may perform (e.g., web_search, summarize)
• Resource limits: Token budgets, time constraints, and domain whitelists
• Sub-delegation depth: max_sub_delegations controls whether the recipient can further delegate work

The MultiAgentPolicyEngine applies role-based policies before any action executes. When TrustLayer.authorize_action() is called, the system evaluates both the agent's static role permissions and the dynamic delegation scope.

Implementing Delegation in Code

Registering Agents with the Trust Layer

Begin by initializing the trust layer and registering both the delegating orchestrator and the receiving specialist:

from advanced_ai_agents.multi_agent_apps.multi_agent_trust_layer.multi_agent_trust_layer import TrustLayer

# Initialize the trust layer

trust_layer = TrustLayer()

# Register orchestrator with high trust for delegation authority

trust_layer.register_agent(
    agent_id="orchestrator-001",
    human_sponsor="alice@company.com",
    organization="Acme Corp",
    roles=["orchestrator"],
    initial_trust=900,
)

# Register specialist that will receive delegation

trust_layer.register_agent(
    agent_id="researcher-002",
    human_sponsor="bob@company.com",
    organization="Acme Corp",
    roles=["researcher"],
    initial_trust=750,
)

The register_agent() method in lines 48-71 of the source file creates the identity and trust score records necessary for subsequent delegation operations.

Creating Delegation Scopes

Define explicit boundaries before creating the delegation. The scope dictionary converts to a DelegationScope dataclass (lines 84-90):

# Define role policies for the researcher

trust_layer.policy_engine.add_role_policy(
    "researcher",
    {
        "base_trust_required": 500,
        "allowed_actions": ["web_search", "read_document", "summarize", "analyze"],
        "denied_actions": ["execute_code", "send_email", "delete_file"],
    },
)

# Create delegation with scoped permissions

delegation_id = trust_layer.create_delegation(
    from_agent="orchestrator-001",
    to_agent="researcher-002",
    scope={
        "allowed_actions": ["web_search", "summarize"],
        "allowed_domains": ["arxiv.org", "github.com"],
        "max_tokens": 50000,
        "max_sub_delegations": 1,  # Permit one level of sub-delegation

    },
    task_description="Research recent AI-safety papers",
    time_limit_minutes=30,
)

The create_delegation() method (lines 270-326) validates that the parent agent has sufficient trust, generates a SHA-256 signature for the delegation, and records the transaction in the audit log.

Executing Delegated Actions

Wrap the receiving agent in a GovernedAgent class that routes every operation through the trust layer's authorization checks:

from advanced_ai_agents.multi_agent_apps.multi_agent_trust_layer.multi_agent_trust_layer import GovernedAgent

# Wrap the specialist agent

researcher = GovernedAgent("researcher-002", trust_layer)
researcher.current_delegation = delegation_id

# Execute allowed action within scope

result_allowed = researcher.execute(
    "web_search",
    {"query": "AI safety breakthroughs 2024"}
)

# Trust score increases for successful in-scope actions

# Attempt disallowed action outside scope

result_denied = researcher.execute(
    "send_email",
    {"to": "boss@example.com", "body": "Report attached"}
)

# Returns success=False, trust score penalized

The authorize_action() method (lines 387-422) evaluates role policies, validates the delegation scope (lines 333-354), and triggers trust score updates through TrustScoringEngine.record_event() (lines 523-567).

Handling Sub-Delegations

When max_sub_delegations is greater than zero, the receiving agent can further delegate to other agents. The system automatically narrows the scope using the DelegationScope.narrow() method (lines 118-128):

# Researcher delegates to assistant (one level deeper)

sub_delegation_id = trust_layer.create_delegation(
    from_agent="researcher-002",
    to_agent="assistant-003",
    scope={
        "allowed_actions": ["web_search"],  # Narrowed: only search, no summarize

        "max_sub_delegations": 0,  # Leaf node cannot delegate further

    },
    task_description="Fetch supporting articles",
    time_limit_minutes=10,
    parent_delegation_id=delegation_id,  # Links to parent for scope validation

)

The DelegationManager validates that the parent's remaining sub-delegation budget permits this operation and that the new scope is a subset of the parent's permissions.

Monitoring Trust and Audit Trails

The trust layer provides complete observability through the TrustScoringEngine and in-memory audit log. Query the current state using:

# Check current trust metrics

score = trust_layer.get_trust_score("researcher-002")
level = trust_layer.get_trust_level("researcher-002")
print(f"Trust Score: {score}, Level: {level.value}")

# Retrieve full audit trail

audit_entries = trust_layer.get_audit_log(agent_id="researcher-002")
for entry in audit_entries:
    print(f"{entry.timestamp}: {entry.event_type} - {entry.details}")

Every action—successful or failed—generates an AuditEntry with timestamps, agent IDs, delegation chains, and trust score deltas. This immutable record supports compliance requirements and forensic analysis when agents violate their delegated scopes.

Summary

• Agent-to-agent delegation in awesome-llm-apps relies on a comprehensive trust layer that binds every operation to verified identities and scoped permissions.
• Registration via TrustLayer.register_agent() establishes the identity and initial trust score required for participation in delegation chains.
• Scoped delegation uses DelegationScope to explicitly limit actions, resources, and sub-delegation depth, with automatic narrowing enforced on transitive delegations.
• Authorization combines role-based policies from MultiAgentPolicyEngine with real-time delegation validation in DelegationManager.validate_action().
• Governance is enforced through the GovernedAgent wrapper, which routes all execution through TrustLayer.authorize_action() and automatically updates trust scores based on compliance.
• Observability comes from comprehensive audit logging and trust score querying, enabling runtime monitoring of delegation chains and agent behavior.

Frequently Asked Questions

What is agent-to-agent delegation?

Agent-to-agent delegation is a design pattern where one autonomous LLM agent (the delegator) assigns specific tasks and authority to another agent (the delegate) under explicitly defined constraints. In the awesome-llm-apps implementation, this process is secured through cryptographic signatures, trust scoring, and scope limitations that prevent delegates from exceeding their granted authority.

How does the trust layer prevent unauthorized actions?

The trust layer prevents unauthorized actions through a multi-stage validation process implemented in TrustLayer.authorize_action(). First, the MultiAgentPolicyEngine evaluates role-based permissions to ensure the agent's static role permits the action. Second, if a delegation is active, DelegationManager.validate_action() checks that the specific action falls within the DelegationScope boundaries. Finally, the TrustScoringEngine records the attempt, penalizing the agent's trust score if any check fails.

Can an agent delegate to multiple other agents simultaneously?

Yes, an agent can maintain multiple active delegations to different agents simultaneously, provided the agent has sufficient trust levels and each delegation is created individually via TrustLayer.create_delegation(). Each delegation receives a unique ID and operates under its own DelegationScope. The delegating agent's trust score influences its capacity to create new delegations, and the system tracks each delegation chain separately in the audit log.

What happens when an agent violates its delegation scope?

When an agent attempts an action outside its delegated scope, the DelegationManager.validate_action() method returns a failure status, and TrustLayer.authorize_action() blocks the operation. Simultaneously, the TrustScoringEngine.record_event() method logs a scope_violation_attempt event and reduces the agent's trust score according to the severity configuration. Repeated violations can drop the agent's trust level to Suspended, automatically invalidating all active delegations and preventing future participation in the trust network.

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