AI Cost Attribution Needed the Same Unit as the Work
Why AI spend becomes more useful when cost is attributed to the same task, subagent, loop, or session boundary where delegated work is reviewed.
// FIELD TRANSMISSIONS
System Logs
An archive of decisions, failures, changes, and shipped work. The record stays useful by keeping the sequence—and the limits—visible.
79entries on record
Filter by domain
Reasoning Depth Needed a Budget
Why reasoning depth in agent systems should be allocated against task complexity, retries, latency, and recovery cost instead of maximized by default.
The Website Exposed an Agent Contract: WebMCP Makes It Concrete
A product-experience analysis of what changes when a website exposes structured tools directly to agents: less interaction guesswork, but a new contract for semantics, authority, validation, and inspectable results.
Long-Running Agent Recovery Starts With State Continuity
Long-running agent recovery depends on preserving execution state, pending decisions, artifacts, and side-effect history rather than simply retrying the last request.
The Context Window Is the Last Mile
A seven-layer diagnostic for tracing context failures upstream of the model window, from selection and representation to retrieval, compaction, permissions, inspection, and final capacity.
Persistent AI Memory Became a User-Controlled Surface
Why persistent AI memory needs a user-governance layer after the system has already decided what is valid enough to remember.
The Evaluation Suite Became a Production Dependency
Why production evaluation needs its own maintenance contract when cases, judges, workload distributions, noise, and failure surfaces change.
The Semantic Failure Never Became an Error
Why some of the most important production agent failures need semantic evaluation and trace review because they never appear as system errors or explicit user feedback.
The Harness Started Changing Itself: Agent Change Control Needs a Baseline
Why an agent that can modify its own harness needs a separate boundary between authoring the next candidate and trusting it as the next baseline.