Executing Dynamic Autonomy
How to take AI agents from proof of concept to production in a real enterprise: architecture, state and memory, knowledge pipelines, the tool gateway, identity, cost, testing, observability and security.
- 117 pages
- PDF, free, no sign-up
- In English
Why we wrote it
Most enterprise agent projects don't stall because the models aren't smart enough. They stall because the software around them assumes everything is deterministic: static service accounts, no cost limits, tests that pass while the agent quietly gets the business logic wrong.
This book is the blueprint we wish we'd had: one running case study, from a brittle legacy estate to governed, observable agents that act on behalf of people and never beyond them.
Who it's for
Systems architects, CTOs, heads of engineering and platform engineers who have to run AI agents against real systems of record — and answer for what they do.
Contents
- Preface: the state of enterprise autonomy
- Introduction and the architecture blueprint
- Agentic design patterns and swarm orchestration
- State, context and the multi-session engine
- The dual-layer knowledge pipeline and graph RAG
- Knowledge graphs and continuous data pipelines
- Event-driven architecture and process discovery
- The MCP gateway: the central switchboard
- Autonomous commerce and sagas
- Identity, auth and permissions: zero-trust agents
- Conquering latency
- Scaling, concurrency and FinOps
- The platform development lifecycle, TDAD and IaC
- Debugging the black box: traces, metrics and logs
- Adversarial security and guardrails
- Conclusion: the future of autonomous code
- Appendices: the Project Titan case study, blueprints for architecture, lifecycle, developer experience, identity, and SRE and agent-to-agent communication
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