Posts tagged Context Engineering — AI × software engineering, made practical.
You will learn how to model, measure and reduce the tokens each agent spends reading, validating and summarising the same shared data, and how to price that spend next to your warehouse bill.
You'll learn how a prompt differs from context, why extra context can make an agent worse, and how to use three questions to decide what stays in and what comes out.
You will learn how the bulkhead pattern isolates shared capacity, starting with classic thread pools and then applying it to LLM agents through per-sub-agent context budgets, tool output caps and summary-only boundaries.
You'll learn how to split an agent run's token allowance by decision type (explore, plan, edit, verify), give each type its own stop rule, and pick the shares from your own trace data.
You'll learn to measure precision@k and distractor rate alongside recall@k, and to choose how much context a coding agent gets based on whether its output passes, not on whether the right file showed up.
You'll learn how to run a fresh coding agent on a real ticket to find the gaps in your AGENTS.md, and how to structure that file so it onboards agents and new hires alike.
You'll learn why coding agents copy the patterns in your existing code instead of following the rules in CLAUDE.md, and how a four-step exemplar audit finds and fixes the files they copy most.
How to model time, status and supersession in your retrieval pipeline so a coding agent stops acting on decisions your team has already reversed.
A team fine-tuned a model to fix a classification bug, celebrated in Slack, and then quietly reverted it — because a rewritten prompt matched it for free and cost nothing to maintain.
When you split a task by role instead of by knowledge, both agents' logs can show flawless reasoning — and the merge still breaks, for a reason neither agent could have caught alone.
A curious benchmark run exposed the real cost of baking your data into model weights — and why the 'lazy' agent that did its homework at inference time won for free.
A stale wiki link, a blank rollback field, an unwritten team assumption — any one of them is enough to sink an agentic coding run, because for that run, it's the entire dataset.