Architecture
Architecture Blog
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Build the Smallest Autonomous Loop That Can Work
A practical way to scope AI autonomy around one observable goal, one bounded action surface, and one trustworthy feedback loop before expanding further.- Published on

Sandboxes Are Capability Containers
Why effective AI sandboxing is about constraining authority, data, tools, networks, and persistence—not merely isolating a process.- Published on

Retrieval Needs Admission Control
Why reliable AI context depends on source authority, freshness, conflict handling, and deliberate exclusion—not merely finding semantically similar documents.- Published on

Structured Outputs Are Protocol Boundaries
Why schemas, validation, repair, and semantic checks are the boundary that turns probabilistic model output into dependable system behavior.- Published on

AI Cost Is an Architecture Problem
Why token budgets, model choice, context assembly, retries, and verification should be designed as part of the system rather than optimized after the bill arrives.- Published on

Tool Contracts Are More Important Than Tool Count
Why reliable agents need narrow semantics, explicit effects, typed failures, and verifiable results more than they need an enormous catalog of tools.- Published on

Model Routing Is a Product Policy
Why choosing a model per task should express product risk, latency, privacy, and quality policy instead of hiding behind a benchmark leaderboard.- Published on

AI Observability Is Decision Reconstruction
How to trace context, model decisions, tool effects, policy, and evidence so agent behavior can be understood and improved after the run.- Published on

Prompt Injection Is a Trust Boundary Problem
Why agent security depends on separating instructions from untrusted content, constraining authority, tracking provenance, and verifying effects outside the model.- Published on