Engineering Agent Systems
Engineering Agent Systems
Most agent education teaches the building blocks: prompts, tools, memory, planning, retrieval, guardrails, and frameworks.
This series focuses on what happens when those pieces become a system.
Agent systems are not just LLMs with tools. They are closed-loop systems operating under uncertainty over time. They must reason, act, track state, recover from failure, and remain controlled while interacting with real environments.
Series Thesis
Agent systems are closed-loop systems operating under uncertainty over time.
This is the worldview behind the series. Production agent engineering is not only about making models smarter. It is about designing the system around the model so it can behave reliably despite uncertainty.
The Five-Part Series
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The Engineering of Uncertainty
Why LLMs are stochastic, why systems need reliability, and why agent engineering begins with uncertainty, time, and state. -
Why Agents Fail: The Execution Gap
Why demos work but production systems fail: state drift, partial execution, tool failure, retries, and runaway loops. -
From DAGs to State Machines
Why linear chains and DAGs are not enough for adaptive agent systems, and how state machines better model agent execution. -
Controlled Agency: Tools, Safety, and Production Readiness
How tool contracts, permissions, action gates, observability, and bounded autonomy make agents safer to deploy. -
Kavriq Recommendations for Building Reliable Agentic Systems
A practical checklist for applying the systems view to production agent design.
How This Connects to Agentic AI Foundations
The 12-part Agentic AI series gives you the foundations: agent architecture, planning systems, tools, memory, multi-agent patterns, evaluation, and runtime internals.
Engineering Agent Systems builds on that foundation by asking a higher-level question:
How do these pieces behave together in production over time?
If the foundations teach the components, this series teaches the system behavior.