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.
Nacho Martinez argues that the model is fixed while the harness (agent loop, memory, context engineering, tools and skills, continual learning) is where engineers can make real gains. He walks through the agent stack and applies it to an autonomous AML agent that flags suspicious bank transactions, then demonstrates the workshop environment and answers questions on memory design, skill retrieval, caching, and hybrid search.
Martin Keen explains why bigger context windows can make AI agents less reliable. He introduces context rot, the four context failure modes of poisoning, distraction, confusion, and clash, and the context engineering fixes that work by removing information rather than adding it.
Gil Feig, co-founder and CTO of Merge, explains why agents need a company-wide context graph rather than a few MCP connections. Using the question of which customers were upset last week, he shows why live API lookups fail across large datasets and why synced local copies, routing, summarization, skills, memory, and traceable provenance are needed to build a reliable company brain.
Matt Gibiec of Dynatrace argues that coding agents are fast but blind without real context about how services run in production, and introduces Bluebox, an observability agent that brings dependency maps, production baselines, and change history into local testing, issue detection, fix suggestions, and pre-build planning, with a human approving the final result.
Elizabeth Fuentes Leone, developer advocate at AWS, explains why agents break when tools return huge outputs, why bigger context windows do not fix the problem because models lose the middle of long contexts, and how four context engineering strategies (externalize, select, compress, isolate) can be implemented with the open-source Strands Agents framework.