Topic
agents
21 posts
- Agents7 min
The child process inherited nothing, so the test now walks every spawn site
Child process environment variables: config moved to a table loaded by each entry point's first import, and three scripts the bot spawns were not entry points. A test now walks spawn sites.
- Agents6 min
Four traps when merging parallel agent worktrees into one trunk
Git worktree for parallel agents: sessions edit one repo in separate worktrees and a person merges by cherry-pick. The traps are the tests, the classifier, patch-ids and the leftovers.
- Agents6 min
A restart sent 30 direct messages in 8 seconds. Then --help sent the daily report three times
Telegram bot duplicate messages, twice in three days: code that reads the live database and talks to a person is only as safe as the rows it finds, and DM is not a fallback.
- Agents6 min
The approval queue said nine fixes were waiting. Eight were already in production
Human in the loop agent approvals: the bot's pending list was its own table while people merged the same branches by hand. The fix was a check at the moment of speaking, not a sync job.
- Agents7 min
I told the agent to search its memory. It made 31 tool calls and searched zero times
LLM agent memory search: a 24-run experiment said telling the agent to search was as good as injecting notes. It measured irrelevant notes against nothing, and nobody searched.
- Evals7 min
44 error alerts, 19 automatic fixes, and no gate on how big a fix could be
AI code review on auto-fixed alerts: the alert-to-deploy path had gates for relevance, kind, review, QA and cost, and none for size. What 19 fixes looked like, and why file count lies.
- Evals9 min
Low effort cut agent turns by two thirds. Accuracy, I could not tell
Claude effort level on 40 tickets, three arms: low effort used fewer turns on 21 of 21 paired tickets, and on accuracy the twin arms disagreed with each other as much as with it.
- Retrieval10 min
The index found 39 percent, the agent found 96, and the useful question was neither
Agentic search vs an index on the same 23 pairs: the index found 39 percent and the agent 96, and per repository the trade ranged from five hits for twenty seconds to nothing at all.
- Agents4 min
The numbers halved and the fix never ran
Deployment verification the hard way: wall clock down 53 percent, tool calls down 47, and the line the fix exists to produce absent in both runs. The config was a symlink shared by every worktree.
- Evals5 min
Five ways to measure the wrong thing, and each one was inside the fix for the last
Evaluation methodology failures, five in a row: a timer that decided nothing, an unmeasurable total, a price that was mostly thinking, a per-stage rate, and a feature returning nothing.
- Agents6 min
The median barely moved and the spread collapsed
Claude Code speed on code review: one line naming the search command. The median barely moved, the worst case fell from 18.0 to 12.2 minutes, and tool calls did not change.
- Evals5 min
The span that answered a question it never measured
OpenTelemetry LLM spans on every tool call told me the agent never ran anything in parallel, and I withdrew it. The numbers were not wrong. I had written them myself.
- Agents9 min
LangGraph yes, LangChain no
LangGraph vs LangChain in an agent pipeline: one earns its place by doing something I would otherwise write badly. The other is a spec I satisfy without importing.
- Agents8 min
Building the guardrails I needed after a session died
Claude Code hooks and a status line for context usage and compaction. Both read the transcript rather than an API field, and that choice is the post.
- Evals9 min
Flaky and consistently wrong are different bugs with the same score
LLM evaluation: flaky and consistently wrong both show up as reduced accuracy. One responds to prompt changes and the other does not, and telling them apart takes one extra run.
- Agents8 min
I instrumented everything except where the decisions happen
LLM agent observability with a connected, green tracing dashboard, and zero traces from the three components that actually make choices.
- Evals10 min
Ground truth from git history answers a different question than you asked
Ground truth evaluation from closed tickets gives you a free answer key. It records what someone once changed, not what needs changing now, and those diverge in specific ways.
- Agents10 min
Context is burned by tool output, not by conversation
Claude Code context usage over a two-month transcript: the largest entries were single command results, around 300,000 tokens each.
- Evals8 min
My agent benchmark was reading the answer off disk
LLM benchmark leakage in an agent eval: the bot produced a 239-line file, the human version on disk was 239 lines, and the diff was empty, filename included.
- Agents9 min
An 80-day agent session and the wall at the end of it
Claude Code context limit on an 80-day session: fifty compactions, each starting from a full million tokens. Then one morning it stopped answering for nineteen minutes.
- Evals9 min
The baseline that beat my agent was answering the same thing every time
LLM agent evaluation without a null baseline: routing accuracy went from 73% to 90%, and a strategy that ignores the input scored 95% on the same test.