The most comprehensive, information-dense collection of resources for harness engineering — the practice of shaping the environment around AI agents so they work reliably in real-world production systems.
Harness engineering sits at the intersection of context engineering, evaluation, observability, orchestration, safe autonomy, and software architecture. While an agent is the model plus its tools, the harness is everything else: the constraints, state management, verification loops, and runtime infrastructure that make agents dependable.
┌─────────────────────────────────────────────────────────────────┐
│ AGENT HARNESS │
│ │
│ ┌──────────┐ ┌──────────┐ ┌───────────┐ ┌───────────────┐ │
│ │ Context │ │ Guardrails│ │ Evals & │ │ Runtime & │ │
│ │ Engine │ │ & Safety │ │Observability│ │ Orchestration │ │
│ └────┬─────┘ └────┬─────┘ └─────┬─────┘ └──────┬────────┘ │
│ │ │ │ │ │
│ └──────────────┴──────┬───────┴───────────────┘ │
│ │ │
│ ┌──────┴──────┐ │
│ │ LLM Agent │ │
│ │ (Model+Tools)│ │
│ └─────────────┘ │
│ │
│ ┌──────────┐ ┌──────────┐ ┌───────────┐ ┌───────────────┐ │
│ │ Memory & │ │ Specs & │ │ Sandbox & │ │ Benchmarks │ │
│ │ State │ │Agent Files│ │ Execution │ │ │ │
│ └──────────┘ └──────────┘ └───────────┘ └───────────────┘ │
└─────────────────────────────────────────────────────────────────┘
Why this list? The shift from prompt engineering → context engineering → harness engineering marks a maturation of the AI engineering discipline. This list tracks that evolution with primary sources, not commentary.
- Foundations
- Context, Memory & Working State
- Constraints, Guardrails & Safe Autonomy
- Specs, Agent Files & Workflow Design
- Evals & Observability
- Benchmarks
- Runtimes, Harnesses & Reference Implementations
- MCP (Model Context Protocol)
- Coding Agents in Practice
- Production Deployment
- Academic Research
- Learning Resources & Curated Lists
- Contributing
- License
The foundational writings that defined harness engineering as a discipline.
| Source | Article | Description |
|---|---|---|
 |
Harness engineering: leveraging Codex in an agent-first world | The article that coined "harness engineering" — how OpenAI built a large application with Codex using architectural constraints, repo-local instructions, browser validation, and telemetry |
 |
Effective harnesses for long-running agents | Core article on initializer agents, feature lists, init.sh, self-verification, and handoff artifacts across many context windows |
|
Harness design for long-running application development | GAN-inspired multi-agent harness — generator/evaluator loop for autonomous frontend design and long-running app generation |
|
Building effective agents | Foundational guide distinguishing workflows vs. agents, with composable patterns for building reliable systems |
 |
The Anatomy of an Agent Harness | Derives harness components from first principles: prompts, tools, middleware, orchestration, and runtime infrastructure |
 |
Harness Engineering | Framing harness work into context engineering, architectural constraints, and "garbage collection" against entropy |
 |
Skill Issue: Harness Engineering for Coding Agents | A practical argument that weak results from coding agents are often harness problems, not model problems |
 |
Your Agent Needs a Harness, Not a Framework | Why state, retries, traces, and concurrency are first-class infrastructure concerns |
|
Unlocking the Codex harness: how we built the App Server | Deep dive into the Codex app server harness implementation |
|
Unrolling the Codex agent loop | Technical breakdown of the Codex agent loop architecture |
Understanding the trajectory from prompt engineering to harness engineering.
- 🔗 The Third Evolution: Prompt → Context → Harness — Epsilla's three-era framework for AI engineering evolution
- 🔗 2025 Was Agents. 2026 Is Agent Harnesses. — Industry overview of the agent-to-harness shift
- 🔗 The Rise of AI Harness Engineering — Overview of the emerging discipline
- 🔗 The importance of Agent Harness in 2026 — CPU-OS analogy; "the harness is the dataset"; trajectory capture as competitive advantage
- 🔗 Harness Engineering: The Missing Layer Behind AI Agents — Clear distinction between prompt/context/harness engineering
- 🔗 Agent Harnesses: From DIY Patterns to Product — Evolution from ad-hoc patterns to productized harnesses
- 🔗 What we miss when we talk about "AI Harnesses" — Philosophical critique of the harness framing
- 🔗 Agent Frameworks, Runtimes, and Harnesses, Oh My! — LangChain's decomposition of what belongs in a framework, a runtime, and a harness
- 🔗 Conductors to Orchestrators: The Future of Agentic Coding — O'Reilly's take on the future of agent-driven development
- 🔗 Martin Fowler on Preparing for AI's Nondeterministic Computing — Engineering for nondeterminism in agent systems
- 🔗 Why AI Agent Reliability Depends More on the Harness Than the Model — HackerNoon on harness-first reliability
- 🔗 What Is an Agent Harness? — Firecrawl's breakdown: infrastructure that makes AI agents actually work
- 🔗 What is an agent harness in the context of LLMs? — The model is CPU, context window is RAM, harness is OS, agent is application
- 🔗 The GAN-Style Agent Loop: Deconstructing Anthropic's Harness — Analysis of generator-evaluator harness pattern
- 🔗 From Prompts to Context to Harness Engineering — Evolution overview
- 🔗 OpenAI Introduces Harness Engineering — InfoQ coverage of the paradigm shift
- 🔗 Agent Engineering: A New Discipline — LangChain on the emergence of agent engineering
- 🔗 Agents At Work: The 2026 Playbook — Building reliable agentic workflows
- 🔗 Anthropic 2026 Agentic Coding Trends Report (PDF) — How coding agents are reshaping development
The art of managing what goes into the context window — treating it as a working memory budget, not a dumping ground.
| Source | Article | Description |
|---|---|---|
|
Effective context engineering for AI agents | Managing the context window as a working memory budget with practical strategies |
|
Context Engineering for Agents | Four strategies: writing, selecting, compressing, isolating context |
|
The Rise of Context Engineering | Why context engineering matters more than prompt engineering |
 |
Context Engineering for AI Agents: Lessons from Building Manus | KV-cache locality, tool masking, filesystem memory, and keeping useful failures in-context |
|
Context Engineering for Coding Agents | Shaping the task environment so coding agents stay grounded and productive |
 |
Architecting efficient context-aware multi-agent framework | ADK's tiered state architecture: Session, Memory, Artifacts |
|
Advanced Context Engineering for Coding Agents | Patterns for reducing context drift and making coding sessions easier to resume |
|
Context-Efficient Backpressure for Coding Agents | Preventing agents from burning context on noisy or low-value work |
 |
Context Engineering: What It Is and Techniques to Consider | LlamaIndex's techniques for context engineering |
| Context Engineering (Lance Martin) | Practical guide from LangChain co-founder | |
| Context Engineering: Bringing Engineering Discipline to Prompts — Addy Osmani | Systematic approach to context management | |
 |
Efficient Context Management for LLM-Powered Agents | NeurIPS 2025 research: simple observation masking ≈ LLM summarization, ~50% cost reduction |
| Context Engineering Best Practices — Comet | Best practices for agentic systems | |
| Context Engineering Best Practices — Kubiya | Practical reliability-focused best practices |
How agents persist knowledge, recover from interruptions, and maintain state across sessions.
| Project | Description |
|---|---|
| Letta (formerly MemGPT) | Stateful agents with OS-like memory management (RAM/disk analogy) |
| Rearchitecting Letta's Agent Loop | Lessons from ReAct, MemGPT, and Claude Code for agent loop design |
| Memory Blocks — Letta | Discrete functional memory units for context window management |
| LangGraph + Redis | <1ms latency state persistence for agents |
| LangGraph + DynamoDB | Durable agent state on AWS infrastructure |
| Checkpoint/Restore Systems for AI Agents | Survey of checkpoint/restore techniques |
| Databricks Agent Memory | Built-in memory for Databricks agent framework |
| Mem0 | Hybrid storage (Postgres + vector); extracts memories with ADD/UPDATE/DELETE operations; up to 26% accuracy gains |
| Zep | Temporal knowledge graph tracking how facts change over time; combines graph memory with vector search |
| Cognee | Knowledge graph layer that structures, connects, and retrieves information as interconnected knowledge |
Libraries that ensure reliable, schema-compliant agent output.
| Project | Description |
|---|---|
| Instructor | Type-safe structured extraction from LLMs using Pydantic; SDKs for Python, TypeScript, Go, Ruby |
| Outlines | FSM-based token masking ensures 100% schema-compliant output at generation time |
Reducing approval friction without losing control — sandboxing, policy design, and quality loops.
| Source | Article | Description |
|---|---|---|
|
Beyond permission prompts: making Claude Code more secure | Better sandboxing and policy design for secure autonomous agents |
|
Code execution with MCP: building more efficient agents | Controlled execution power through explicit, inspectable tool boundaries; 150K → 2K token reduction |
|
Writing effective tools for agents | Tool interfaces that are easier for models to call correctly and safely |
|
Advanced tool use on the Claude Developer Platform | Tool Search, Programmatic Tool Calling, and Tool Use Examples |
|
Assessing internal quality while coding with an agent | Moving quality checks into the loop instead of relying on after-the-fact review |
|
Anchoring AI to a reference application | Constraining agents with concrete exemplars for more consistent output |
|
Humans and Agents in Software Engineering Loops | Where humans should strengthen the harness instead of micromanaging artifacts |
|
Claude Code: Best practices for agentic coding | Repo structure, checkpoints, validation, and delegation in agentic workflows |
| Agentic Engineering Patterns — Simon Willison | Coding practices and patterns for working with agents | |
| The lethal trifecta for AI agents — Simon Willison | Private data + untrusted content + external communication = security risk | |
| Governing Claude Code with Kong AI Gateway | Secure agent harness rollouts via API gateway | |
| AI Agent Safety — Cleanlab | Managing unpredictability at scale in production agents | |
| Lessons from 2025: Agent Mitigation | How "agent mitigation" became a new discipline |
| Project | Description |
|---|---|
| Invariant Guardrails (now Snyk) | Rule-based guardrailing for MCP and agentic AI |
| Invariant MCP-scan | Security scanner for MCP servers: prompt injection, tool poisoning detection |
| NeMo Guardrails — NVIDIA | Open-source programmable guardrails; sub-100ms latency; GPU-accelerated |
| Guardrails AI | Open-source framework for LLM output validation |
How to tell agents what to do — repo-local instruction files and machine-readable specifications.
| Project | Description |
|---|---|
| AGENTS.md | Open format for repo-local instructions; intro by OpenAI |
| agent.md | Related standardization effort for machine-readable agent instructions |
| GitHub Spec Kit | Toolkit for spec-driven development — agents execute against explicit specs |
| Writing a good CLAUDE.md | Practical guide to creating durable, repo-local instructions; 150–200 instruction limit |
| Equipping agents with Agent Skills — Anthropic | SKILL.md-based progressive disclosure system for domain-specific agent capabilities |
| How to write a great agents.md — GitHub | Lessons from 2,500+ repositories |
| awesome-agents-md | Curated list of real-world AGENTS.md files, templates, guides & tools |
| awesome-agent-skills | 1,000+ agent skills from official dev teams and community |
| CLAUDE.md vs AGENTS.md vs .cursorrules | Comparison of agent configuration file formats |
| Source | Article | Description |
|---|---|---|
|
12 Factor Agents | Operating principles for production agents: explicit prompts, state ownership, clean pause-resume |
| 12-Factor AgentOps | Operations-oriented companion focused on context discipline and reproducibility | |
|
Understanding Spec-Driven-Development | Why strong specs make AI-assisted delivery more dependable |
|
How we built our multi-agent research system | Orchestrator-worker pattern with parallel subagents; 90%+ improvement over single-agent |
|
Developer's guide to multi-agent patterns in ADK | Multi-agent orchestration patterns in Google ADK |
|
Introducing AgentWorkflow | Multi-agent orchestration system |
|
Workflows 1.0 | Event-driven framework for agentic workflows |
| Emerging Patterns in Building GenAI Products — Martin Fowler | Architecture patterns for generative AI products | |
| Agent-Native Engineering | Engineering practices for agent-first development |
How to measure whether your agent actually works — evaluation methodology for non-deterministic systems.
| Source | Article | Description |
|---|---|---|
|
Testing Agent Skills Systematically with Evals | Turning agent traces into repeatable evals with JSONL logs and deterministic checks |
|
Agent evals | Measuring agent quality with reproducible task-level and workflow-level evaluations |
|
Evaluation best practices | Building eval suites that match real-world distributions and catch regressions |
|
Trace grading | Grading agent traces directly, especially for long multi-step tasks |
|
Demystifying Evals for AI Agents | What to measure when agents have many possible trajectories |
|
Quantifying infrastructure noise in agentic coding evals | Runtime configuration can move benchmark scores more than many leaderboard gaps |
|
Evaluating Deep Agents: Our Learnings | Single-step, full-run, and multi-turn eval design for stateful agents |
|
Improving Deep Agents with harness engineering | Top 30 → Top 5 on Terminal-Bench 2.0 by only changing the harness |
|
How we build evals for Deep Agents | Eval methodology for LangChain's deep agents |
|
How Middleware Lets You Customize Your Agent Harness | Middleware patterns for loop detection and custom harness behavior |
| 8 benchmarks shaping the next generation of AI agents | Overview of key agent benchmarks |
| Platform | Type | Description |
|---|---|---|
| Arize Phoenix | OSS | OpenTelemetry-based tracing, evals, and experiments for AI |
| Langfuse | OSS | LLM observability: tracing, prompt management, evals (MIT license) |
| LangSmith | Commercial | Agent engineering platform: tracing, evaluation, deployment |
| Braintrust | Commercial | AI observability + evaluation; used by Notion, Stripe, Zapier |
| Helicone | Commercial | AI Gateway with routing, caching, rate limiting, cost analytics |
| AI observability tools buyer's guide 2026 | Guide | Comprehensive comparison of observability platforms |
| Portkey | Commercial | AI gateway + observability; routing, fallbacks, load balancing, caching, and prompt versioning |
| LiteLLM | OSS | Unified proxy for 100+ LLMs in OpenAI format; cost tracking, guardrails, load balancing |
| OpenTelemetry for LLMs | Standard | Emerging standard; OpenLLMetry and OpenLIT emit OTLP-compatible spans |
| Comparing open-source AI agent frameworks | Guide | Framework comparison with observability perspective |
Benchmarks that stress harness quality, not just model quality — context handling, tool calling, environment control, verification logic, and runtime scaffolding.
| Benchmark | Focus | Description |
|---|---|---|
| SWE-bench Verified | 🔧 Code | Real GitHub issues and tests; harness choices around retrieval, patching, and validation are highly visible |
| SWE-PolyBench — Amazon | 🔧 Code | Multi-language: 2,110 instances across 21 repos in Java/JS/TS/Python |
| SWE-Bench Pro | 🔧 Code | 1,865 problems from 41 repos and 123 programming languages |
| FeatureBench | 🔧 Code | 200 eval instances; SOTA agents achieve only 11% (vs 74% on SWE-bench) |
| Terminal-Bench | 💻 Terminal | Terminal-native agents in shells, filesystems, and verification-heavy environments |
| Terminal-Bench 2.0 & Harbor | 💻 Terminal | Harder tasks and generalized evaluation harness |
| OSWorld | 🖥️ Desktop | 369 tasks across Ubuntu, Windows, macOS with execution-based evaluators |
| AppWorld | 🌐 Interactive | Controllable world of apps for testing planning, code generation, and collateral-damage control |
| AgentBench | 🌐 Multi-env | Cross-environment: OS, databases, knowledge graphs, web browsing |
| tau2-bench | 🔄 Multi-step | Realistic multi-step tasks where success depends on tool use and execution quality |
| WebArena-Verified | 🌐 Web | Curated web-agent tasks with deterministic evaluators over responses and network traces |
| WorkArena | 🌐 Enterprise | Common knowledge-work tasks on realistic enterprise-style web workflows |
| GAIA | 🤖 General | General AI assistant benchmark for tools, planning, verification, and long-horizon autonomy |
| HAL: Holistic Agent Leaderboard | 📊 Leaderboard | Reliability, cost, and broad task coverage for comparing end-to-end harness behavior |
| DPAI Arena — JetBrains | 🔧 Code | Open platform for coding agent benchmarks across full dev lifecycle |
| LOCA-bench | 🧠 Context | Benchmarks long-context agents; reveals "context rot" phenomenon |
| SWE-bench Live | 🔧 Code | Live benchmark with real-time GitHub issues |
| Tool | Description |
|---|---|
| Inspect AI | UK AI Safety Institute's eval framework; batteries-included with pre-built benchmarks |
| ai-agent-benchmark-compendium | Compendium of 50+ agent benchmarks, categorized by function calling, reasoning, coding |
| Galileo Agent Eval | Framework with metrics, rubrics, and benchmarks for production agent evaluation |
| Framework | Maintainer | Description |
|---|---|---|
| Claude Agent SDK | Anthropic | Production-oriented SDK with sessions, tools, orchestration, and compact feature |
| OpenAI Agents SDK | OpenAI | Visual canvas + Agent Builder + ChatKit + Connector Registry |
| Google ADK | Open-source framework for building multi-agent applications | |
| Microsoft Agent Framework | Microsoft | Convergence of AutoGen + Semantic Kernel; checkpointing & resuming |
| AutoGen | Microsoft | Open-source multi-agent programming framework |
| LangGraph | LangChain | Graph-based agent orchestration with built-in persistence |
| deepagents | LangChain | Deeper, longer-running agents with middleware and harness patterns |
| CrewAI | CrewAI | Role-driven multi-agent orchestration; fastest-growing for multi-agent |
| MetaGPT | Open Source | Simulates software company with PM/Architect/Engineer/QA agents |
| Pydantic AI | Pydantic | Type-safe Python agent framework |
| Agno | Agno | High-performance multi-agent runtime |
| Smolagents | Hugging Face | Ultra-minimal agent framework |
| Mastra | Gatsby team | JavaScript agent framework |
| AWS Strands Agents | AWS | Model-driven ReAct pattern; deep Lambda integration |
| AgentKit | Inngest | TypeScript toolkit for durable, workflow-aware agents |
| Vercel AI SDK | Vercel | Unified toolkit for 30+ LLM providers; frontend-to-backend agent infrastructure |
| VoltAgent | VoltAgent | TypeScript agent platform with orchestration, memory, RAG, and enterprise observability |
| Platform | Description |
|---|---|
| E2B | Open-source Firecracker microVM sandboxing; ~150ms cold starts |
| Modal | Container-based agent execution; scales to 50K+ concurrent instances |
| Daytona | Docker-based sandbox; sub-90ms creation; pivoted to agent infra in 2025 |
| SWE-ReX | Sandboxed code execution infrastructure for AI agents |
| awesome-sandbox | Curated list of code sandboxing solutions for AI agents |
| Browserbase | Cloud-hosted browser instances for AI agents at scale |
| Stagehand | Browserbase's open-source SDK bridging Playwright and AI agents |
| Firecrawl | Managed isolated browser environment + web scraping API for agents |
| Top AI Code Sandbox Products — Modal | 2025 comparison of sandbox solutions |
| Project | Description |
|---|---|
| SWE-agent | Mature research coding agent with inspectable harness, prompt, tools, and environment |
| Harbor | Generalized harness for evaluating and improving agents at scale |
| Terminal-Bench | Open-source terminal benchmark implementation |
The emerging standard for giving agents structured, controlled access to tools and data sources.
| Resource | Description |
|---|---|
| MCP Specification (2025-11-25) | Latest protocol specification |
| 2026 MCP Roadmap | Priorities: remote deployment, auth, enterprise features |
| MCP Roadmap Growing Pains — The New Stack | Production challenges and planned solutions |
| MCP Servers | Official reference server implementations |
| MCP Auth Spec Updates — Auth0 | Authentication additions to MCP |
| MCP + Codex — OpenAI | How Codex integrates with MCP |
| MCP.so | Marketplace/directory for MCP servers; 1,000+ live connectors |
| Context7 MCP | Provides LLMs with up-to-date, version-specific documentation and code examples |
| awesome-mcp-servers | Most popular community-curated list of MCP servers |
| Tool | Type | Description |
|---|---|---|
| Claude Code | CLI | Anthropic's agentic coding CLI with hooks, sub-agents, and MCP |
| Codex | CLI | OpenAI's cloud-based coding agent |
| Cursor | IDE | AI-first code editor with Background Agents |
| Windsurf | IDE | Cascade engine for agentic coding workflows |
| Aider | CLI | Open-source AI pair programming in the terminal |
| Continue | Extension | Open-source AI code assistant for VS Code and JetBrains |
| OpenHands | Platform | Open platform for AI software developers |
| Gemini CLI | CLI | Google's open-source AI agent for the terminal |
| Devin | Platform | Cognition's autonomous coding agent |
| Replit Agent | Platform | In-browser agent with snapshot engine and self-healing tests |
| Goose | CLI | Block's fully open-source (Apache-2.0) MCP-native agent; model-agnostic |
| Cline | Extension | BYOM (bring your own model) agent for VS Code |
| Devon | CLI | Open-source pair programmer with autonomous planning and debugging |
| OpenCode | CLI | 75+ provider support, LSP integration, privacy-first |
| v0 | Platform | Vercel's AI-powered frontend development agent |
Real-world insights from teams building and deploying coding agents at scale.
| Source | Article | Key Insight |
|---|---|---|
|
A practical guide to building agents | Comprehensive guide covering use case selection, design patterns, guardrails |
|
Building an AI-native engineering team | Guide for teams adopting agent-first development |
|
OpenAI Cookbook — Agents | Collection of agent-related code examples and tutorials |
 |
Coding Agents 101 | Practical guide to working with coding agents effectively |
|
Devin's 2025 Performance Review | Learnings from 18 months of agents at work; task scoping insights |
|
Rebuilding Devin for Claude Sonnet 4.5 | Context management insights from model migration |
|
How Cognition Uses Devin to Build Devin | Self-referential agent development case study |
 |
Decision-Time Guidance | Injecting situational instructions at key moments vs. front-loading |
|
Inside Replit's Snapshot Engine | Reversible compute and storage fabric for agent safety |
|
Introducing Agent 3 | Self-healing testing, 200-minute autonomous runtime |
 |
Introducing the new v0 | Sandbox-based runtime, Git workflow integration |
 |
Ranking Engineer Agent (REA) | Autonomous AI agent accelerating Meta's ads ranking engineering |
|
Closing the knowledge gap with agent skills | How agent skills help bridge domain knowledge gaps |
| My LLM Coding Workflow Going into 2026 — Addy Osmani | Practical coding workflow with agents | |
| The Cognition: Devin is in the Details — swyx | Deep dive into Devin's architecture | |
|
Introducing Agent 4 | Parallel task execution, multi-platform development |
|
Building agents with the Claude Agent SDK | Production-oriented SDK; compact feature for context management |
Lessons from running agents in production — what breaks, what works, and what scales.
| Source | Article | Key Finding |
|---|---|---|
| AI Agents in Production 2025 — Cleanlab | Survey of 1,837 respondents; only 95 with agents live in production | |
| Key Findings from 1,200 Production Deployments — ZenML | 95% of agent deployments fail; system fragility, not model intelligence | |
| The State of Agentic AI in 2025: A Year-End Reality Check | Industry reality check on agent deployment | |
| Building Production-Grade AI Agents — Towards AI | Complete technical guide for production agents | |
| Building Reliable Autonomous Agentic AI — TechEmpower | Practical reliability patterns | |
|
State of Agent Engineering | Survey of 1,300+ professionals on agent engineering challenges |
|
Lessons from 2025 on agents and trust | Google Cloud CTO lessons on agent deployment and trust |
| Harness Engineering 101: Claude Code / Codex Workflows | Practical reproducible, safe, long-running workflows |
Papers advancing the theoretical and empirical foundations of harness engineering.
| Paper | Venue/Date | Key Contribution |
|---|---|---|
| Building Effective AI Coding Agents for the Terminal | arXiv, Mar 2026 | OpenDev agent; scaffolding vs. harness architecture distinction |
| Natural-Language Agent Harnesses | arXiv, Mar 2026 | Harness-level control via natural language: roles, contracts, verification gates |
| Agentic Context Engineering (ACE) | arXiv, Oct 2025 | Contexts as evolving playbooks; 14.8% improvement over ReAct |
| Meta Context Engineering via Agentic Skill Evolution | arXiv, Jan 2026 | Bi-level framework where meta-level agent refines engineering skills |
| Context Engineering for AI Agents in Open-Source Software | arXiv, Oct 2025 | Study of context engineering file adoption in 466 open-source projects |
| PAACE: Plan-Aware Automated Agent Context Engineering | arXiv, Dec 2025 | Context engineering as a learnable, plan-aware optimization problem |
| The Complexity Trap | NeurIPS 2025 | Simple observation masking ≈ LLM summarization; ~50% cost reduction |
| Paper | Venue/Date | Key Contribution |
|---|---|---|
| Memory Management for Long-Running Low-Code Agents | arXiv, Sep 2025 | Memory management for persistent agent sessions |
| Efficient On-Device Agents via Adaptive Context Management | arXiv, Nov 2025 | Context management for resource-constrained environments |
| Agentic AI: Challenges and Opportunities | arXiv, Jan 2026 | Comprehensive survey of verifiable planning, coordination, memory, governance |
| From Competition to Coordination: Safe Multi-Agent LLM Systems | arXiv, Nov 2025 | Market-making framework for safe multi-agent coordination |
| Emergent Coordination in Multi-Agent Language Models | arXiv, Oct 2025 | How prompt design steers multi-agent LLMs |
| Towards a Science of AI Agent Reliability | arXiv, Feb 2026 | Evaluates 14 models across 3 providers with scaffolding strategies |
| Confucius Code Agent | arXiv, Dec 2025 | Scalable agent scaffolding with persistent note-taking for cross-session learning |
| Agentic AI Frameworks: Architectures, Protocols, Design | arXiv, Aug 2025 | Comprehensive survey of agentic AI architectures and protocols |
| A Practical Guide for Production-Grade Agentic AI Workflows | arXiv, Dec 2025 | Nine best practices: tool-first design, single-responsibility agents, KISS principle |
| Paper | Venue/Date | Key Contribution |
|---|---|---|
| Towards a Science of Scaling Agent Systems | DeepMind, Dec 2025 | Scaling multi-agent systems scientifically |
| Measuring Agents in Production | arXiv, Dec 2025 | Framework for measuring agent performance in production |
| Evaluation and Benchmarking of LLM Agents: A Survey | arXiv, 2025 | Comprehensive survey of agent evaluation methods |
| Harnessing Multi-Agent LLMs for Complex Engineering | arXiv, Jan 2025 | Multi-agent framework for engineering design projects |
| Multi-Agent Coordination: A Survey | arXiv, Feb 2025 | Survey of coordination mechanisms across domains |
| Resource | Description |
|---|---|
| awesome-agent-harness | Curated list of agent harness resources |
| walkinglabs/awesome-harness-engineering | The original awesome list for harness engineering |
| Context-Engineering handbook | First-principles handbook inspired by Karpathy |
| Awesome-Context-Engineering | Comprehensive survey: hundreds of papers, frameworks, guides |
| yzfly/awesome-context-engineering | Curated papers, tools, and best practices for context engineering |
| learn-claude-code | Reverse-engineers Claude Code's harness mechanisms session by session |
| harness-engineering (deusyu) | Learning guide from concept to practice |
| Harness Engineering Academy | Tutorials, career guides, and learning paths |
| agent-engineering.dev | Articles on harness engineering as a production discipline |
| harness-engineering.ai | Complete guide to agent harness concepts |
| Prompt Engineering Guide: Context Engineering | Community reference guide |
| ACE-FCA (HumanLayer) | "Frequent intentional compaction" approach; tested on 300K LOC Rust codebase |
| Resource | Description |
|---|---|
| awesome-ai-agents-2026 | 300+ resources across 20+ categories, updated monthly |
| awesome-agents (kyrolabs) | Open-source tools and products to build AI agents |
| awesome-ai-agent-frameworks | Most up-to-date list of AI Agent Frameworks |
| awesome-cli-coding-agents | Terminal-native agents and harnesses |
| awesome-vibe-coding | Curated list of vibe coding references |
| awesome-claude-code | Tools, IDE integrations, frameworks for Claude Code |
| awesome-copilot | GitHub's official awesome-copilot with AGENTS.md |
| Awesome-LLMOps | LLMOps tools for developers |
| Resource | Description |
|---|---|
| awesome-ai-agents-security | Living map of AI agent security ecosystem by security lifecycle |
| awesome-ai-guardrails | Curated materials on AI guardrails |
| Resource | Description |
|---|---|
| awesome-ai-agent-papers | Curated 2026 AI agent research papers, updated weekly from arXiv |
| Awesome-Agent-Papers | Up-to-date LLM Agent survey: methodology, applications, challenges |
| Awesome-Self-Evolving-Agents | Comprehensive survey of self-evolving AI agents (2023–2025) |
| Autonomous-Agents | Autonomous Agents research papers, updated daily |
| KDD 2025 Tutorial: Evaluation of LLM Agents | Two-dimensional taxonomy of evaluation objectives and processes |
Contributions are welcome! Please prefer resources that are:
- Primary sources — original implementations, first-party articles, or seminal papers
- Specific — about how agents are constrained, evaluated, resumed, observed, or orchestrated
- Practical — useful to practitioners building real harnesses, not generic AI commentary
- Current — actively maintained or recently published (2024+)
If two links say the same thing, prefer the more primary, practical, and implementation-oriented one.
See CONTRIBUTING.md for contribution guidelines and the preferred entry format.