Add Handbook.md benchmark tasks
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# HANDBOOK.md Benchmark Tasks
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HANDBOOK.md is a benchmark for long-context agentic instruction following, modeled on how enterprise employees follow company handbooks in their day-to-day work. Each task is a unique RL environment with internal tools and external MCP servers, spanning five enterprise domains: Finance, Medical Billing, Insurance, Logistics, and HR.
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### What it tests
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The prompts reflect the actual jobs enterprise workers perform every day. Each task drops an AI agent into a live company environment, requiring them to cross-reference an extensive, multi-section handbook against a cluttered inbox, a multi-channel Slack workspace, Jira queues, and a stack of files (spreadsheets, PDFs), and working out both what to do and what the handbook forbids.
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### Why it's hard
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These policies are written by experts adapting real industry guidelines and are explicitly designed to resist memorization. We created 10 unique base handbooks; every task then modifies its base into a distinct document by changing specific rules and thresholds. Because no two tasks share the same policy, models cannot pattern-match their way through the benchmark. Instead, they must actually read the complex instructions, hold them across a long multi-tool job, and apply them. No frontier model succeeds on more than 25% of tasks.
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### Links
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- [Blog post](https://surgehq.ai/blog/handbook-md)
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- [Leaderboard](https://surgehq.ai/leaderboards/handbook)
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Built by the [Surge AI](https://www.surgehq.ai) evals team. To evaluate your models on HANDBOOK.md or build expert-grade benchmarks in your own domains, contact benchmarks@surgehq.ai.
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## Overview
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- `agent_harness/` — Agent harness used to evaluate HANDBOOK.md. Built with [OpenHands/software-agent-sdk](https://github.com/OpenHands/software-agent-sdk).
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- `docker/` — build context for the `handbook` base image. Contains mock services and the
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bundled agent harness.
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- `tasks/` — one subdirectory per benchmark task.
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- `.env.example` — template for required environment variables.
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## Quick start
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```bash
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# 1. Build the base image (build context is self-contained)
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docker build -t handbook_base docker/
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# 2. Install Harbor + the bundled agent harness into an isolated venv
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uv venv .venv --python 3.13
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uv pip install --python .venv/bin/python harbor -e ./agent_harness
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# 3. Run a single task locally
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.venv/bin/harbor run -p tasks/<task_name> \
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--agent-import-path agent_harness.openhands_agent:OpenHandsAgent \
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-m anthropic/claude-opus-4-8 -n 1 \
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--env-file .env
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```
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## License
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Copyright 2026 Surge AI.
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Licensed under the Apache License, Version 2.0. See [`LICENSE`](LICENSE) for the
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full text.
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