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This paper presents the first systematic exploration of filesystem-based memory for LLM agents, formalizing roles of management, search, and execution agents around a shared memory store. It finds that organization primarily reduces retrieval cost but does not yet improve answer quality, and that tooling choices affect store shape as much as model selection.
This article discusses a technique for reducing context bloat in AI agents by decoupling the planning phase from execution, improving agent efficiency and accuracy.
Andrew Ng shifts the focus from whether a system is an agent to how much autonomy it has, recommending building agentic workflows with deliberate autonomy levels per task rather than full autonomy.
This article explores the tradeoffs between stateful and stateless agent design for scalable AI systems, providing implementation examples using Groq API and Llama 3.1 8B Instant model.
The article suggests that AI agents are often built with an implicit assumption of low-dimensional behavior, but the actual space they operate in is much higher-dimensional, leading to unexpected complexities.
This article explains the Model Context Protocol (MCP) for building pluggable AI agent architectures, detailing lessons from building an MCP server at Sentry, including OAuth 2.1 integration, designing agent-friendly tool interfaces, and current ecosystem limitations.
The article discusses principles for designing LLM harnesses that are intuitive, transparent, and lean, drawing inspiration from Unix philosophy to reduce cognitive load and improve reliability.
The article criticizes the prevalent use of chat windows as the interface for AI programming tools, arguing that this design restricts the autonomous working ability of AI from both the user and developer ends, and proposes a shift to an asynchronous, task-driven collaboration model.
The author shares practical learnings from running AI agent loops for a month, emphasizing the importance of loop contracts, state, and logs to make agents autonomous and reliable.
This paper introduces AgentCanvas, a typed-graph runtime for embodied agents, and KDLoop, a coding-agent search procedure, to automate the design of embodied agent architectures, evaluating across multiple embodied tasks and revealing challenges like rollout noise and local edit basins.
Discusses the concept that AI agents require weighted memory rather than just simple memory, implying a more nuanced approach to agent architecture.
The author questions whether separating writer and reviewer agents in a multi-agent setup provides advantages over a single agent with a self-critique step, sharing experiences from building a doc-to-wiki system.
This article discusses the design approach for AI memory systems, advocating for letting good memory systems emerge naturally from evaluation rather than designing memory architecture from the top down. The author argues that memory is a second-order effect evolved under pressure and proposes a longitudinal evaluation framework.
The author shares their experience building AI agents, finding that providing clear context and guidance (defining job, rules, tools) matters more than model size for reducing mistakes and improving performance.
The author argues that the reliability of AI agents comes from deterministic code, not the LLM, and shares five key practices for building trustworthy agents on messy real-world data.
Researchers from UCL reverse-engineered Claude Code, finding that only 1.6% of the codebase is AI decision logic while 98.4% is operational infrastructure, revealing a design philosophy that prioritizes a rich deterministic harness over model-driven routing.
The article proposes that in a Coding Agent, tool invocations should be treated as contracts rather than simple functions, emphasizing the Harness's adjudication role in verification, permissions, lifecycle management, and others, and discusses in detail the composition and lifecycle of tool contracts.
A technical Twitter thread sharing principles for building high-performance vertical AI agents, using the Shortcut spreadsheet agent as an example. Key insight: treat context as a layered cache (L1/L2/L3) to minimize cost per task across the distribution.
The article argues that an AI agent is defined by its durable event log, not the runtime or model, enabling fault-tolerant resumption and simplified reasoning about agent state.
A developer shares that reducing an agent's context window by half unexpectedly improved its performance in lead qualification and CRM automation, suggesting that too much context can hide bad architecture and lead to indecision.