Agent-to-Agent Authentication: The Missing Piece in AI Workflows

Multi-agent systems are revolutionizing AI workflows. But there's a critical gap: how do agents authenticate with each other?

The Multi-Agent Challenge

Consider a typical AI workflow:

1. Planning Agent breaks down tasks
2. Research Agent gathers information<br>
3. Analysis Agent processes data
4. Writing Agent generates final output

Each agent needs to call LLM APIs, but:

• 🔴 Sharing API keys is insecure
• 🔴 Token expiration breaks workflows
• 🔴 No audit trail of which agent did what
• 🔴 Manual token management doesn't scale

The Attach Gateway Approach

Attach Gateway solves this with secure token delegation:

1. Initial Authentication

# User authenticates once
attach = AttachClient(user_jwt)
headers = {"Authorization": "Bearer " + user_jwt}

2. Agent Token Exchange

# Agent requests a scoped token
agent_token = await attach.get_agent_token(
    original_token=user_jwt,
    agent_id="research-agent",
    scopes=["llm:read", "memory:write"]
)

3. Secure Agent Calls

# Agent uses its own token
response = requests.post(
    "http://gateway:8080/api/generate",
    headers={"Authorization": f"Bearer {agent_token}"},
    json={"prompt": "Research topic X"}
)

Benefits

🔐 Security

• Each agent has its own token
• Scoped permissions per agent
• Token lifecycle hooks (rotation coming soon)

📊 Observability

• Full audit trail of agent actions
• Request attribution by agent
• Usage analytics per agent

🚀 Scalability

• No manual token management
• Works with multiple agents
• Integrates with existing OIDC providers

Implementation Example

class AIWorkflow:
    def __init__(self, user_token):
        self.attach = AttachClient(user_token)
        # Initialize your agent implementations
        self.research_agent = ResearchAgent()  # research-agent
        self.analysis_agent = AnalysisAgent()  # analysis-agent
        
    async def run_analysis(self, topic):
        # Get agent tokens with consistent scopes
        research_token = await self.attach.get_agent_token(
            agent_id="research-agent",  # Research Agent
            scopes=["llm:read", "memory:write"]
        )
        
        analysis_token = await self.attach.get_agent_token(
            agent_id="analysis-agent",  # Analysis Agent
            scopes=["llm:read", "memory:write"]
        )
        
        # Agents work independently with their own tokens
        research = await self.research_agent.run(
            topic, token=research_token
        )
        
        analysis = await self.analysis_agent.run(
            research, token=analysis_token
        )
        
        return analysis

Real-World Use Cases

Customer Support Bot

1. Intake Agent (intake-agent) → Initial user query
2. Knowledge Agent (knowledge-agent) → Search company docs
3. Response Agent (response-agent) → Generate answer
4. Escalation Agent (escalation-agent) → Route to human if needed

Content Generation Pipeline

1. Planning Agent (planning-agent) → Create content outline
2. Research Agent (research-agent) → Gather supporting data
3. Writing Agent (writing-agent) → Generate draft
4. Review Agent (review-agent) → Quality check and edit

Getting Started

Ready to secure your multi-agent workflows?

pip install attach-dev
# Configure with your OIDC provider
export OIDC_ISSUER=https://your-domain.auth0.com
attach-gateway --port 8080

Learn More

• Quick Start Guide
• Multi-Agent Examples
• Join our Discord

Building multi-agent systems? We'd love to hear about your use case. Share your story in our Discord community!