Multi-agent systems introduce safety challenges that single-agent architectures do not face, including privilege escalation through delegation, cascading failures across agent chains, and the difficulty of auditing actions across multiple autonomous actors. SafeClaw by Authensor addresses these with per-agent policy isolation, unified hash-chained audit trails, and deny-by-default gating at every node. Install it with npx @authensor/safeclaw to scale your safety architecture alongside your agent architecture.

Why Multi-Agent Safety Is Different

In a single-agent system, safety is straightforward: one agent, one policy, one audit trail. In a multi-agent system, new problems emerge:

Privilege escalation through delegation. Agent A has permission to read files but not execute commands. Agent A delegates a task to Agent B, which has command execution permissions. Agent A has effectively escalated its privileges through delegation. Without per-agent isolation, the system's effective permissions are the union of all agents' permissions.

Cascading failures. One misbehaving agent can trigger actions in downstream agents. A research agent that returns manipulated data can cause an analysis agent to make incorrect decisions, which causes an action agent to execute harmful operations. Each agent in the chain needs independent safety controls.

Audit complexity. When something goes wrong in a multi-agent system, you need to trace the causal chain across agents. A unified audit trail that covers all agents with tamper-evident logging is essential for root cause analysis.

Inconsistent safety postures. Different agents may have been built by different teams using different frameworks. Without a unified safety layer, each agent may have different (or no) safety controls.

SafeClaw's Multi-Agent Architecture

SafeClaw provides per-agent policy isolation while maintaining a unified safety framework:

[ Orchestrator ]
      |
  ┌───┼───┐
  |   |   |
[A] [B] [C]  <-- Each agent has its own SafeClaw policy
  |   |   |
[SafeClaw instance per agent]
  |   |   |
[Unified audit trail]

Each agent operates under its own deny-by-default policy. Agent A's permissions are completely independent of Agent B's permissions. Delegation from A to B does not transfer A's permissions to B or vice versa.

Step-by-Step Migration

Step 1: Inventory Your Agents

List every agent in your system and its intended capabilities:

| Agent | Role | Needed Permissions |
|---|---|---|
| Research Agent | Gather information | Network reads, file reads |
| Analysis Agent | Process data | File reads/writes, computation |
| Action Agent | Execute changes | Shell commands, file writes |
| Review Agent | Validate output | File reads |

Step 2: Install SafeClaw

npx @authensor/safeclaw

Step 3: Define Per-Agent Policies

Create a separate policy for each agent based on the principle of least privilege:

# research-agent-policy.yaml
rules:
  - action: "network:request"
    host: "api.approved-source.com"
    effect: "allow"
  - action: "file:read"
    path: "/workspace/data/**"
    effect: "allow"
  # No file write, no shell execute, no other network

# action-agent-policy.yaml
rules:
  - action: "shell:execute"
    command: "npm test"
    effect: "allow"
  - action: "file:write"
    path: "/workspace/output/**"
    effect: "allow"
  # No network access, no reads outside output

Step 4: Initialize Per-Agent SafeClaw Instances

Each agent gets its own SafeClaw instance with its own policy:

const researchSafety = safeclaw.create({
  agentId: 'research-agent',
  mode: 'enforce',
  policy: './research-agent-policy.yaml',
  audit: true
});

const actionSafety = safeclaw.create({
  agentId: 'action-agent',
  mode: 'enforce',
  policy: './action-agent-policy.yaml',
  audit: true
});

Step 5: Gate Inter-Agent Communication

When agents communicate or delegate tasks, the receiving agent's SafeClaw instance evaluates the action independently. Agent A cannot grant Agent B permissions that B's policy does not allow.

Step 6: Enable Unified Audit

Configure all SafeClaw instances to write to a unified, hash-chained audit trail. Each entry includes the agent ID, so you can trace actions across the entire system:

safeclaw.configureAudit({
  unified: true,
  hashChain: true,
  includeAgentId: true
});

Step 7: Simulate Before Enforcing

Run the entire multi-agent system in simulation mode. Observe inter-agent interactions. Identify actions that need to be permitted and actions that should remain blocked. Adjust per-agent policies before switching to enforcement.

Preventing Privilege Escalation

The key safeguard against privilege escalation in multi-agent systems is that SafeClaw evaluates actions at the executing agent, not the requesting agent. When Agent A asks Agent B to run a command, Agent B's policy determines whether the command is allowed. Agent A's broader or narrower permissions are irrelevant.

This architecture means that the maximum damage any single compromised agent can cause is limited to its own policy. A compromised research agent with read-only permissions cannot cause writes or command execution, even if it manipulates other agents' inputs.

Monitoring Multi-Agent Systems

The unified audit trail enables monitoring patterns specific to multi-agent systems:

• Unusual delegation patterns (Agent A suddenly sending requests to agents it normally does not interact with)
• Repeated denied actions (an agent persistently attempting actions outside its policy)
• Cascading action chains (a sequence of actions across agents that warrants review)

Related reading:

• Migration Guide: Adding SafeClaw to an Existing AI Agent


• How to Switch from Allow-by-Default to Deny-by-Default


• SafeClaw Features: Everything You Get Out of the Box


• The Complete Guide to AI Agent Safety (2026)