Signal SentrySearchUpdated Oct 28, 05:30 UTCAM Drop
Frontier watch · Long-horizon reliability

Harnesses for hours- and days-long agents

What harness designs keep agents productive and recoverable across many context windows, hours or days of work?

Why it matters

Most long-run failures are harness failures: lost state, a premature 'done', no recovery when a container dies. The labs now sell their harnesses as managed services, so the designs they publish shape what builders inherit.

State of play

Newest first. Each claim is our summary of the linked primary source, with a short quote from it.

  1. ResearchOct 15, 2026Source

    Researchers report in a preprint that four Gemini 4 Argon agents, coordinating through in-game chat and shared memory, wrote 2,783 programs and beat Minecraft's Ender Dragon in 27 hours and 14 minutes.

    Starting from a randomly generated world on Easy difficulty, the team wrote 2,783 programs and defeated the dragon in 27 hours and 14 minutes, dying 605 times along the way.

    What it does not show: One run, the first launch tried, so it cannot show reliability; the agents played through code on the Mineflayer library, not a screen.

  2. DocumentedSep 10, 2026Source

    OpenAI's API changelog says the Agents API entered public beta, with OpenAI running a managed Codex harness that handles sessions, context compaction and recovery.

    Build agents with a managed Codex harness while OpenAI handles session orchestration, context compaction, and recovery.

    What it does not show: Public beta; the changelog gives no figures on run length or failure rates.

  3. AnnouncedApr 22, 2026Source

    Google Cloud says its Gemini Enterprise Agent Platform lets customers deploy long-running agents that run autonomously for days at a time.

    You can now deploy long-running agents that run autonomously for days at a time.

    What it does not show: Launch post; no data on how often multi-day runs succeed.

  4. DocumentedApr 8, 2026Source

    Anthropic says Managed Agents, its hosted service for long-horizon work, virtualizes an agent into a session log, a harness loop and a sandbox.

    We virtualized the components of an agent: a session (the append-only log of everything that happened), a harness (the loop that calls Claude and routes Claude’s tool calls to the relevant infrastructure), and a sandbox (an execution environment where Claude can run code and edit files).

    What it does not show: Vendor engineering post; no reliability or uptime figures for long runs are given.

  5. ResearchMar 24, 2026Source

    Anthropic reports a three-agent harness (planner, generator, evaluator) that produced full-stack applications over multi-hour autonomous coding sessions.

    The final result was a three-agent architecture—planner, generator, and evaluator—that produced rich full-stack applications over multi-hour autonomous coding sessions.

    What it does not show: Internal experiments; the example full-harness run took 6 hours and cost $200, versus 20 minutes and $9 solo.

  6. ResearchFeb 5, 2026Source

    An Anthropic researcher reports that an agent team built a 100,000-line C compiler that builds Linux 6.9, over nearly 2,000 Claude Code sessions and $20,000 in API costs.

    Over nearly 2,000 Claude Code sessions and $20,000 in API costs, the agent team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V.

    What it does not show: One researcher's experiment on a task with strong existing tests to steer against; not a general result.

  7. DocumentedNov 26, 2025Source

    Anthropic says the key to its long-running coding harness was letting each fresh-context session recover the state of work from a progress file plus git history.

    The key insight here was finding a way for agents to quickly understand the state of work when starting with a fresh context window, which is accomplished with the claude-progress.txt file alongside the git history.

    What it does not show: Tested on building a web app clone; Anthropic notes failures such as declaring features done without end-to-end testing.

What would change the picture

Signals we are watching for. Our own reading of where the thread could go: not predictions, and not sourced claims.

  • A lab publishes success or recovery rates for multi-day runs on its managed agent service Would suggest: Long-running agents moving from demos to measured operations
  • A lab removes a harness feature (resets, planners) because a new model no longer needs it Would suggest: Reliability shifting from scaffolding into the model
  • A common session or checkpoint format is adopted across two or more vendors' agent services Would suggest: Long-running agent state becoming portable infrastructure

From the Drops

AgentsDocumented

LangChain's Slack design agent made over 3,000 assets; median cost fell to about 50 cents

LangChain says Content Studio's median request cost dropped from about $8 in mid-September, three in four requests now come from outside marketing, and it removed most hard-coded design rules after Opus 5.5 arrived.

Why it matters: A lesson for harness builders: rules that patch model weaknesses expire, LangChain says, while the environment, context and an approval step before anything ships last.

LangChain · Oct 27
From Drop #051 · Wed, Oct 28 · Morning
AgentsResearch

Preprint: 37.4% of agent apps that must keep state lose it when the container restarts

Across 433 deployed open-source agent systems, researchers checked 14 contracts from LangGraph and platform docs. Of those needing saved state, 41.5% lost it on container replacement; only two in three fixes fully worked.

Why it matters: If your agents pause for human input or resume saved work, test restarts and container swaps before production. The authors found 78% of fixes changed app code.

arXiv · Oct 27
From Drop #049 · Tue, Oct 27 · Morning
AgentsResearch

Preprint: context compaction broke 5-64% of 22 coding-agent successes

Compact-Bench pairs full-history and summary-only runs across 14 summarizer models. LLM summaries kept at most 16.0% of recurring details, versus 79.1% for mechanical extraction, the authors report.

Why it matters: Long-running coding agents lean on summaries to keep going. The authors say retaining relevant information alone does not ensure the agent can finish the task.

arXiv · Oct 26
From Drop #047 · Mon, Oct 26 · Morning
AgentsResearch

Preprint: DeLoop cuts repeat-action loops in long-running agents from 57.3% to 6.1%

Researchers report agents often re-issue identical tool calls, more so once history is compressed. Training on their own loops cut Qwen3-8B tasks with loops from 57.3% to 6.1% on CodeGym, using about a third as many steps.

Why it matters: Loops waste tokens in long runs. The method needs no task rewards or LLM judges, though the abstract reports results only on Qwen models; on Qwen3-32B success rose from 37.7% to 44.8%.

arXiv via Hugging Face · Oct 14
From Drop #044 · Sat, Oct 24 · Evening
AgentsResearch

Preprint: agents that drop their own KV cache cut cost 27.7% vs summarizing

Researchers propose cache engineering: agents drop or reposition cached context instead of summarizing history. A rolling-drop policy raised average benchmark scores 1.7 points and cut cost 27.7%, the abstract says.

Why it matters: Long agent runs get expensive as context grows. The authors pair the method with an AKV serving engine, suggesting the serving layer, not just prompts, can help.

Hugging Face Papers · Oct 20
From Drop #039 · Thu, Oct 22 · Morning
AgentsResearch

Preprint: CarryOn cuts long-horizon agent tokens by 51.9% on OSWorld 2.0

CarryOn organizes an agent's context around reusable task objects instead of compressed histories. The authors report gains of 5.8% on OSWorld 2.0 and 7.0% on BrowseComp-Plus, with token use down 51.9% and 22.9%.

Why it matters: Long agent runs keep re-reading how they got somewhere; this preprint tests carrying forward the progress itself, a design question for any multi-hour harness.

Hugging Face Papers · Oct 13
From Drop #033 · Mon, Oct 19 · Morning