Topic
Context Window Management
7 episodes
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AREX: Towards a Recursively Self Improving Agent for Deep Research
Pippa and Tyler dig into AREX, a recursively self-improving deep research agent from BAAI that alternates between an inner search loop and an outer constraint-verification loop — and discuss whether that architecture is genuinely novel or a smarter repackaging of ideas the field already had.
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Towards a Science of Scaling Agent Systems
Onyx and Echo examine “Towards a Science of Scaling Agent Systems,” a controlled study of when multi-agent architectures help, when coordination becomes a liability, and why task structure matters more than simply adding agents.
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Overview: Context Window Management
We finally slow down and explain Context Window Management from the ground up, because we keep hand-waving it whenever agents, memory, cost, and long tasks come up. The whole thing is the fixed-desk problem: what stays on the desk, what gets compressed, and what falls off.
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The Microsoft Agent Framework Harness is now released | Microsoft Agent Framework
Microsoft Agent Framework has released a stable, batteries-included agent harness for Python and .NET, packaging planning, memory, tool loops, approvals, context compaction, and telemetry behind a configurable agent wrapper.
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Building Governed Agents: A Framework for Cost, Control, and Compliance
Vince and Ava examine LangSmith’s framework for governed agents, focusing on the LLM gateway as a runtime control plane for model choice, cost, permissions, evidence, and continuous improvement.
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Long Horizon Terminal Bench: Testing the Limits of Agents on Long Horizon Terminal Tasks with Dense Reward Based Grading
Laura and Harper dig into Long-Horizon-Terminal-Bench, a new benchmark exposing the gap between short-task agent demos and real multi-hour workflows. They break down the dense-reward grading system, the staggering token costs (9.9M per task), and why current models are failing at sustained execution despite high step-level competence.
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How to Use RLMs in Deep Agents
Exploring Recursive Language Models (RLMs) and their implementation in Deep Agents for handling long contexts efficiently.