Presents a GAN-inspired three-agent architecture (planner, generator, evaluator) for long-running application development, demonstrating how separating generation from structured evaluation with sprint contracts dramatically improves quality over single-agent approaches.

OpenAI's architecture for turning isolated model calls into sustained agent workflows by combining the Responses API with shell tools, persistent containers, and native context compaction.

A detailed reverse-engineering of Claude Code's agent architecture, revealing how a single-threaded master loop with flat message history, structured tool interfaces, async steering, and TODO-based planning delivers controllable autonomy without multi-agent complexity.

Google's comprehensive whitepaper distinguishing agents from plain models, explaining how tools, orchestration layers, and cognitive architectures combine to create capable AI agents.

A comprehensive overview of the major design patterns for building AI agents, from single-agent architectures to multi-agent collaboration, with clear diagrams and practical trade-offs for each pattern.