A Practical Architecture Guide
A question-driven series for engineers, architects, AI leads, and compliance officers. Read the preface first — it explains why most teams discover the governance gap the expensive way, and what this series does about it.
Most teams discover the governance gap the expensive way. This preface sets out why AI agents require a different kind of operational discipline and what the rest of this series covers.
3 min · Updated June 2026
Read article →How exactly are agents different from traditional software -- and why does that difference change what you need to build operationally?
The three ways AI agents break every assumption of traditional software, the three disciplines needed to operate them responsibly, and the reference stack used throughout this series.
6 min · Updated June 2026
Read article →Every time we tweak a prompt or swap a model, something breaks somewhere else. How do we catch regressions before customers see them?
Evaluation as a CI gate, golden datasets, agentic metrics that score the trajectory rather than just the output, and real-world examples across banking, healthcare, and retail.
5 min · Updated June 2026
Read article →It passed all our tests but still does unexpected things with real users. How do we see what it is actually doing?
Distributed tracing for agents using OpenTelemetry and Langfuse, prompt management as a release-controlled artifact, and how production traces become the next evaluation golden dataset.
5 min · Updated June 2026
Read article →How do we prevent agents from taking unauthorized or dangerous actions -- not just detect them afterward?
Runtime policy enforcement at the action boundary, cryptographic agent identity, execution sandboxing, and reliability engineering -- the four preventive controls every production agent needs.
7 min · Updated June 2026
Read article →Regulators and auditors want proof our agents are governed. What do we actually hand them?
Tamper-evident audit trails and compliance evidence, closing the runtime content-guardrails gap, the clean ownership boundary between DeepEval and Langfuse, and how to govern agents in a multi-cloud environment.
7 min · Updated June 2026
Read article →We cannot build all of this at once. What do we do first, and what can wait?
OpenTelemetry as the unified backbone, the continuous-improvement feedback loop, a staged adoption sequence ordered by risk, the agent governance maturity model, and the failure modes most teams underestimate.
8 min · Updated June 2026
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