Agents

A long-running agent has to keep learning without forgetting what made it useful. This write-up covers catastrophic forgetting and how it is measured, the four families of fixes, memory in token space, continual RLVR, the production data flywheel, its guardrails, the multi-LoRA serving shape, and how to run the same loop with Claude.

How to tell whether an LLM system works, and how to keep it working: test cases, graders, judges, groundedness, CI gates, live signals, traces, latency, schemas, injection defense, and PII masking.

An agent that gets better on its own has to change something about itself, and something has to check the change. This post walks the six things it can change, the checker that decides whether any of it worked, the ceiling it runs into, the ways it breaks, and the loop teams actually run in production.

Forty production questions on agentic systems, answered plainly: evals, guardrails, quality measurement, observability, security, and token economics.

What graph engineering is, how it differs from loops and workflow engines, when to build one, a refund approval worked through end to end, and the failure modes to watch for.