Context management

Oct 16, 2026

High Taste Testers: OpenAI Shipped Everything. What Actually Works?

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.

Oct 05, 2026

Agent Memory Is Solved. Agent Learning Isn't. — Karthik Ranganathan, Yugabyte

Karthik Ranganathan of Yugabyte discusses the critical challenge of shared learning among AI agents, arguing that while per-agent memory is solved, true collective intelligence is not. He details Yugabyte's struggles in building agentic systems, highlighting the "lost context" problem and debunking five common misconceptions about agent memory. Ranganathan introduces Meko, an agent-native persistence layer designed to provide shared, governed, and traceable context through "data packs," drastically improving RAG pipeline faithfulness while reducing context size and token costs. Heather Downing demonstrates Meko's resumability and sharability features, showcasing its ability to foster collaborative learning and capture tribal knowledge across agents.

Sep 07, 2026

Self-Improving Harnesses, Local Personal AI And YC's Agent For Work | YC Paper Club

A deep dive into the evolving world of LLM harnesses, demonstrating how they've transformed from simple prompt engineering into sophisticated self-improving systems. The discussion covers architectural advancements (e.g., Prime Agent's context as cache, Turing vs. Von Neumann), practical applications (auto-researchers, on-device personal AI like OpenJarvis, YC's QM for work), and the challenges and future directions of agentic AI. It emphasizes that harness innovation is key to unlocking significant performance gains and enabling LLMs to operate autonomously and adaptively.

Aug 29, 2026

Agents Are Where Microservices Were in 2015 — Roberto Milev & Uday Kanagala, Navan

Roberto Milev and Uday Kanagala from Navan discuss the challenges and emerging best practices in building and operating AI agents, drawing parallels with microservices. They highlight the shift to stateful runtimes, innovative memory and context management using 'skills,' and crucial solutions for observability and testing non-deterministic systems. A central theme is the critical need for robust authorization and guardrails, given the blurring line of agentic action, and a preference for mastering single-agent loops before scaling to complex orchestrations. They conclude by outlining the solved and unsolved problems, with cost and debugging remaining significant hurdles.

Aug 17, 2026

Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI

Experiments with an AI tutor reveal that not compacting context often outperforms summarization, leading to better recall, lower cost, and faster responses, primarily due to LLM API prompt caching. Compaction invalidates the cache, making it unexpectedly expensive. While cloud environments benefit from full history, local hardware limits necessitate compaction, highlighting the need to prioritize actual constraints over default compaction strategies.

Aug 15, 2026

Exo: Harnesses should see their own code and logs — Alex Krentsel

An introduction to Exo, a fully recursive AI agent harnessing a novel three-layer architecture (Executor, Harness, Sandbox) designed for autonomous self-improvement. It delves into how Exo surpasses current agent limitations by allowing the agent to edit its own code and policy at runtime, ensuring protected state and isolated execution, and discusses practical implications and the underlying systems philosophy enabling this paradigm shift.