Monitoring, Deployment & Observability

Objective

UA1 provides production-grade tools for deploying, monitoring, and maintaining synthetic agents in real-world environments. Every agent is treated as a live, auditable microservice—with real-time telemetry, automated deployment workflows, and rollback-ready state checkpoints.

This layer ensures that synthetic labor is traceable, measurable, and trustworthy—both technically and economically.

Deployment Pipeline

Stage	Description
CI/CD Integration	GitHub Actions or GitLab CI compatible pipelines for agent build + test
Containerization	Agents are deployed as OCI-compliant containers via Docker or Firecracker
Orchestration	Deployments managed via ArgoCD, Helm charts, or Kubernetes jobs
Version Control	Agents and skills are versioned independently; semantic patching enforced
Staging Environments	Dev/stage/prod deployment flows with optional canary execution

Monitoring & Observability

Layer	Stack / Tools
Metrics	Prometheus + Grafana dashboards (agent uptime, job success rate, XP gains)
Logging	Loki + OpenTelemetry logs with trace IDs per agent + action
Event Alerts	Custom triggers for high error rates, behavioral drift, skill crashes
Health Checks	Liveness + readiness probes for container uptime
Telemetry Hooks	Per-agent hooks to log execution time, memory load, and output deltas

Observability Outputs per Agent

• Task-level trace logs

• Memory graph diffs (pre/post execution)

• Skill execution profiles

• XP history timeline

• Reputation trajectory visualizations

Deployment Lifecycle (with CLI)

bashCopierModifier# Build image
ua1-cli build --agent ./my-agent

# Deploy to staging
ua1-cli deploy --env staging

# Run synthetic mission test
ua1-cli simulate --input test_input.json

# View real-time logs
ua1-cli logs --agent shopping_bot_01 --tail

# Promote to production
ua1-cli promote --from staging --to production

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Recovery & Rollback

Scenario	Recovery Strategy
Skill failure	Hot-swap with previous skill version (rollback on skill hash)
Behavioral anomaly	Quarantine agent + freeze current memory state
Security scope violation	Terminate runtime, raise alert to governance system
Deployment regression	Automatic rollback to last stable container

Best Practices

Practice	Benefit
Separate environments	Avoid polluting prod with experimental agent behavior
Telemetry standardization	Easier cross-agent comparison and alerts
Synthetic testing	Validate agent logic across edge cases before promotion
Fail-safe design	Graceful exit on runtime timeout, resource overflow, or auth error

Diagrams to Include

• Full CI/CD + Deployment Pipeline (from code commit to running agent)

• Monitoring Stack Layers: Logs, Metrics, Traces → Dashboard Output

• Incident Flow: Agent Crash → Alert → Isolation → Patch → Recovery

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