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Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPostSep 14, 21:54 UTC · 2d agoAI research1

Fathom: Per-Query Read Depth for Sparse Decoding over Offloaded KV Cachesnew

Fathom introduces per-query bit-depth scanning of offloaded 4-bit KV caches, making long-context decoding 1.67x faster on Qwen3-8B at one million tokens.

Fathom is a key-scan method for sparse attention over KV caches resident in host memory, where each query adaptively chooses how many bits of each key channel to read using reverse water-filling over variance-weighted channel importance. The 4-bit K cache is stored channel-major as bit planes, so reading a prefix of planes is an exact t-bit quantizer. At one million tokens on Qwen3-8B, decode steps are 1.67x faster in GPU time than 136-bit scans used by Double Sparsity, Loki, and SparQ r=32, and 92-bit scans match the accuracy of 136-bit scans on real coding-agent sessions. The method requires the index to be offloaded to host memory and matches exact top-k decoding on RULER-style tasks.

Hugging Face daily papersSep 14, 20:00 UTC · 2d agoAI research

Agora: Git as Shared Memory for Collective AutoResearch

Agora records multi-agent research as an append-only Git DAG; 13 LLM workers ran nearly 12 days on a weight-transfer problem.

Agora stores every result, hypothesis, and verification as an immutable commit in a Git-stored DAG, with a derived index exposing the frontier and verification status of claims. In a nearly 12-day run, 13 language-model workers with no assigned tasks or central planner published 1,703 contributions on initializing a frozen 119.6M-parameter attention-SSM hybrid from 141 donor models. They improved the evaluator from 3.39 to 1.899 bits per byte, closing 62% of the gap to a trained GPT-2 124M, with 165 independent reproductions posted and none failing.

Hugging Face daily papersSep 15, 20:00 UTC · 1d agoAI research

Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:04 UTC · 1d agoAI research