Martin Keen explains loop engineering as the next step beyond prompt engineering: designing systems where AI agents act, observe, adjust, and repeat toward verifiable goals. The discussion covers goal design, triggers, context and persistent state, tools via MCP and APIs, verifier subagents, and why humans must remain in the loop to manage comprehension debt, intent debt, and cognitive surrender.
A roundtable of AI power users reviews OpenAI DevDay and asks what actually works in daily use. Topics include personal assistants and their trust, permission, and safety trade-offs; durable agent state and remote execution; the Jev decision model and calibrated classification; model preferences across Anthropic, OpenAI, xAI, Meta, and open models; subagents and context management; and the gap between impressive capabilities and products that hold up.
The panel examines the rise of decision models, a family of LLM-based systems that return a single, scored classification in one forward pass, and asks whether they are genuinely new or a smarter use of existing architectures. It then weighs the cost and benefit of multi-agent teams, drawing on a study that found agent teams cost two to five times more without consistently better results, and closes with Anthropic's new policy against sustained cruelty toward its models and what it says about AI safety, ethics, and human behavior.
A hands-on walkthrough of Claude Managed Agents, building a research desk for SEC filing analysis with a chat UI on the session event stream, skills, subagents, custom tools, shared memory, rubric-graded outcomes and vault-based credential injection.
A Q&A from the final part of Anthropic's Claude Managed Agents workshop, covering CI/CD for agent configs in YAML with the ant CLI, eval practices, workspace-based resource sharing and tenant isolation, agent-driven code review, GPU-enabled self-hosted sandboxes, multiplayer Slack agents, long-running sessions, and the coordinator pattern for custom tools.