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Working agentic AI systems, one per free Lightning session: AI employees that resolve billing issues, closed only when the ledger agrees

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Agentic AI Lightning sessions

The code from each free Lightning session on agentic AI. Each session builds one working system for a named company, checks it against a source of truth outside the model, and measures it with a test you can run offline.

Session What you build Code
Build a Mission Control for AI Agents AI employees work KaaS's GitHub issues. A reply to a customer waits for a signed-in support approver. A card closes only when GitHub shows the reply. mission-control/
Build a Multi-Agent System with MCP A trip planner, a booker and a messenger rebook a cancelled flight, each through an MCP gateway that lists only its tools. The card closes only when the travel system holds a ticket that lands in time and both contacts were told. multi-agent-mcp/
Secure AI Agents with Guardrails A screener shortlists payroll applicants, and one CV hides an instruction in white text. A guardrail withholds that CV from the model and holds the card for a recruiter; a request that screens by age stops before the model is called. guardrails/
Cut AI Agent Costs with Token Economics KaaS Insurance reads accident photos and repair quotes into claim files. Three levers cut the cost per claim filed, and a claim over its budget waits for the adjuster. token-economics/
Build a Second Brain with Claude Code Claude Code files a consultant's notes into quoted Markdown, and an agent answers client questions from them over MCP. A question no note holds waits for the consultant. second-brain/
Build AI Agent Evals from Production Traces KaaS Pharma's labelled production runs gate a model swap. A swap that misses a side effect is refused, and one that passes waits for the medical information lead. evals-from-traces/
Trace AI Agents in Production KaaS Dairy's agent is killed mid shift and resumes from its Langfuse spans without booking a repair twice. A shift over its budget waits for the maintenance lead. trace-in-production/
Build an AI Agent Governance Framework KaaS Hotels' four AI employees work under one policy file. A write over a limit waits for the named signer, and every write has one decision behind it. governance/

Later sessions are listed in sessions.toml and get a folder here once they are built.

factory/ keeps the teaching material of the earlier Software Factory sessions, as an archive with no gate.

make check                         # every built session's gate, offline: no keys, no spend
make -C mission-control demo FRESH=1

Each folder has its own README, .env.example and make check. Python with uv.

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Working agentic AI systems, one per free Lightning session: AI employees that resolve billing issues, closed only when the ledger agrees

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