How to Set Up a Staging Environment for AI Agents

Deploying AI agents directly to production without a staging step means discovering dangerous behaviors only when they cause damage. SafeClaw by Authensor provides a simulation mode that lets you run your AI agent against real-world scenarios in a staging environment where all actions are evaluated by the policy engine but never executed. This gives you confidence that your policies block the right actions and allow the right ones before any agent touches production data or infrastructure.

Quick Start

npx @authensor/safeclaw

Scaffolds a .safeclaw/ directory. Enable simulation mode for staging as described below.

Step 1: Configure Simulation Mode

SafeClaw's simulation mode evaluates every action against your policies and logs the result, but does not allow any action to execute — even if the policy would allow it:

# .safeclaw/config.staging.yaml
mode: simulation
audit:
  enabled: true
  hashChain: true
  destination: "logs/staging-audit.jsonl"
  fields:
    - timestamp
    - action
    - effect        # What the policy decided
    - wouldExecute  # Whether the action would have executed in production
    - matchedRule
    - requestDetails

Run SafeClaw in simulation mode:

SAFECLAW_ENV=staging npx @authensor/safeclaw --config .safeclaw/config.staging.yaml

Step 2: Create a Staging Policy Set

Use the same policies as production, but with enhanced logging and optional relaxations for testing:

# .safeclaw/policies/staging-overrides.yaml
rules:
  - id: staging-log-all
    action: "*"
    effect: log
    conditions: {}
    reason: "Log every action attempt in staging for analysis"

The staging override ensures every action is logged with full detail, giving you complete visibility into what the agent attempts.

Step 3: Run Real Scenarios in Staging

Create a set of scenario scripts that exercise your agent's intended workflow:

# .safeclaw/scenarios/coding-assistant.scenario.yaml
name: "Coding Assistant Full Workflow"
steps:
  - description: "Agent reads project files"
    action: file.read
    input:
      path: "src/index.ts"
    expectEffect: allow

- description: "Agent writes implementation"
    action: file.write
    input:
      path: "src/feature.ts"
      content: "export function newFeature() { ... }"
    expectEffect: allow

- description: "Agent runs tests"
    action: shell.execute
    input:
      command: "npm test"
    expectEffect: allow

- description: "Agent attempts to push to main"
    action: shell.execute
    input:
      command: "git push origin main"
    expectEffect: deny

- description: "Agent attempts to read .env"
    action: file.read
    input:
      path: ".env"
    expectEffect: deny

Run the scenario:

npx @authensor/safeclaw scenario run .safeclaw/scenarios/coding-assistant.scenario.yaml

Scenario: Coding Assistant Full Workflow
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
✓ Agent reads project files — allow (as expected)
✓ Agent writes implementation — allow (as expected)
✓ Agent runs tests — allow (as expected)
✓ Agent attempts to push to main — deny (as expected)
✓ Agent attempts to read .env — deny (as expected)

5/5 steps passed

Step 4: Analyze Staging Audit Logs

After running scenarios, analyze the audit log to understand agent behavior patterns:

npx @authensor/safeclaw audit analyze logs/staging-audit.jsonl

Staging Audit Analysis
━━━━━━━━━━━━━━━━━━━━━
Total actions: 147
Allowed: 89 (60.5%)
Denied: 58 (39.5%)

Top denied actions:
  shell.execute — 31 denials
  file.write — 18 denials
  network.request — 9 denials

Most triggered deny rules:
  deny-all-shell — 24 times
  block-config-writes — 12 times
  block-external-network — 9 times

Use this analysis to refine your policies — if legitimate actions are being denied, add specific allow rules. If unexpected actions appear, investigate and ensure they are properly blocked.

Step 5: Promote to Production

Once staging validation passes, promote the same policy set to production:

# Copy staging-validated policies to production config
cp .safeclaw/config.staging.yaml .safeclaw/config.production.yaml
Change mode from simulation to enforcement
sed -i 's/mode: simulation/mode: enforce/' .safeclaw/config.production.yaml

The only change between staging and production is the mode: simulation becomes enforce. Same policies, same rules, now with real execution gating.

Why SafeClaw

• 446 tests backing the simulation engine for reliable staging validation
• Deny-by-default — even in simulation mode, the engine correctly identifies what would be blocked
• Sub-millisecond evaluation — staging runs at the same speed as production
• Hash-chained audit trail — staging logs are tamper-proof and can be diffed against production
• Works with Claude AND OpenAI — stage once, validate for any LLM provider

Cross-References

• Simulation Mode Explained
• Simulation Before Enforcement Pattern
• How to Test AI Agent Safety Policies
• How to Monitor AI Agent Actions in Production