ClawOps – Agentic AI for DevOps

Foundations

• LLMs demystified: tokens, context windows, temperature, embeddings
• RAG (Retrieval Augmented Generation) — why context is everything
• Agents vs. chatbots vs. copilots — the critical distinction
• Tool calling, function calling, and the MCP protocol explained
• The agent loop: Observe → Think → Act → Reflect
• Cost awareness — tokens aren’t free: budgeting AI in production

Kubernetes Agent

• Architecture: an agent that watches cluster events, logs, and metrics in real time
• Real-time anomaly detection and autonomous remediation
• Live demo: agent detects OOMKill → investigates root cause → scales/patches → reports
• Giving an agent access safely — RBAC, guardrails, blast radius controls
• Integration with Prometheus & Grafana as the agent’s “eyes”
• Runbook-as-code: agent reads your runbooks and executes them as actual remediation steps

CI/CD Agent

• An agent that understands Jenkins pipelines end-to-end
• Auto-diagnosis of failed builds with full contextual awareness
• The auto-fix workflow: diagnose → fix → commit → PR → human approval
• Coverage gap detection: agent finds untested code, writes tests, submits PR
• Security scanning agent finds CVEs in dependencies, auto-creates upgrade PRs with risk assessment

MCP & Developer Context

• MCP architecture: bridging VS Code ↔ Git ↔ Infrastructure ↔ Monitoring
• Building MCP servers that expose your internal tools to agents
• Live demo: developer asks a question → agent pulls context from repo + prod logs + Jira + Confluence
• The paradigm shift: agents that truly understand your system, not just give generic answers
• Custom MCP servers for internal APIs — wrapping your tooling so agents can consume it

The Central

• Architecture: an orchestrator chatbot that coordinates specialized sub-agents
• Routing logic — how does the brain decide which agent to invoke?
• Shared memory and context propagation between agents
• Real-time dashboard: what agents are doing, approval queues, audit trails
• Safety patterns: human-in-the-loop, kill switches, escalation chains
• Escalation flows: agent → agent → human, with full context handoff

Voice Interface

• Voice-to-text pipeline feeding directly into the agent system
• Live infrastructure status checks: “How’s production doing right now?”
• Architecture walkthrough of a podcast-style voice agent
• Real-time TTS responses with infrastructure summaries
• Proactive voice alerting — the agent calls you when something critical happens and briefs you like a human SRE

Guardrails, Observability & Operations

• Guardrails & safety: blast radius limits, dry-run modes, approval gates
• The trust ladder: read-only → suggest → auto-act with approval → full autonomy
• Observability of agents themselves: decision logging, token spend dashboards, drift detection
• Cost & token economics: real-world numbers, caching strategies, model selection (big vs. small)
• Multi-tenancy & team adoption: RBAC for agents, per-team customization, rollout playbook

Wrap-Up & The Road Ahead

• Lessons learned and pitfalls to avoid
• What NOT to automate (yet)
• Where this is heading in 12 months
• Open Q&A

What You Walk Away With

• Working agent architectures, you can deploy in your own environment
• MCP server templates for common DevOps tools
• Runbook-as-code framework for K8s remediation
• CI/CD agent pipeline integration patterns
• Voice interface starter kit
• Central orchestrator design patterns