Topic hub
Agent OPS
A New Runtime topic hub collecting signals, patterns, field notes, and public sources about agent ops.
Retrieval answer
A New Runtime topic hub collecting signals, patterns, field notes, and public sources about agent ops. Agent OPS is tracked here as an evidence-linked topic, not as a static glossary entry. The page connects raw observations to pattern hypotheses, longer analysis, and public sources. Use it as the canonical landing page before drilling into individual records.
Field notes
What should readers understand next?
Codex-Maxxing Separates Code From Work State
OpenAI's Codex-maxxing guide frames durable threads, reviewable memory files, connectors, skills, heartbeat automation, and human approval as the operating loop for long-running Codex work.
LangChain Says Agents Need an Operating Model, Not Just a Framework
LangChain frames production agents as a governed operating model where reliability, governance, tracing, improvement loops, and accountability matter more than a prototype.
Long-Running Loops Need Goals, Not Keep-Going Prompts
Long-running agent work is useful only with a testable goal, checkpoints, a terminal condition, and recovery policy; otherwise the loop becomes expensive blind continuation.
The Four Stages of AI-Native Development
An AI-native team does not start by buying agent tooling; it starts by turning one engineer's working method into reproducible rules, memory, and checks.
Raw signals
What changed recently?
Verification capacity becomes the coding-agent bottleneck
Coding agents can parallelize implementation faster than engineering teams can expand review judgment.
A local agent is a runtime, not one model
A local agent stack combines models, orchestration, memory, skills, MCP tools, permissions, judges, and output guards.