Heavy Scaffolding / Agentic Systems

heavy-scaffolding (E401)

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id	title	type	relationship
provable-safe	Provable / Guaranteed Safe AI	concept	—
dense-transformers	Dense Transformers	concept	—

Frontmatter

{
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Raw MDX Source

---
title: "Heavy Scaffolding / Agentic Systems"
description: "Analysis of multi-agent AI systems with complex orchestration, persistent memory, and autonomous operation. Includes Claude Code, Devin, and similar agentic architectures. Estimated 25-40% probability of being the dominant paradigm at transformative AI."
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llmSummary: "Comprehensive analysis of multi-agent AI systems with extensive benchmarking data showing rapid capability growth (77.2% SWE-bench, 5.5x improvement 2023-2025) but persistent reliability challenges (45-60% error propagation rates, 2:1 human advantage at 32-hour tasks). Estimates 25-40% probability of paradigm dominance at transformative AI, with 67% Fortune 500 deployment but only 6% full trust for core processes."
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clusters: ["ai-safety", "governance"]
entityType: intelligence-paradigm
---
import {Mermaid, EntityLink, DataExternalLinks} from '@components/wiki';

<DataExternalLinks pageId="heavy-scaffolding" />

## Quick Assessment

| Dimension | Assessment | Evidence |
|-----------|------------|----------|
| **Current Capability** | Moderate-High | Claude Sonnet 4.5 achieves 77.2% on SWE-bench Verified; WebArena agents improved from 14% to 60% success rate (2023-2025) |
| **Reliability** | Low-Moderate | Multi-agent systems show 50%+ failure rates on complex tasks; error propagation remains key bottleneck |
| **Safety Profile** | Mixed | Scaffold code is auditable, but autonomy amplifies scope of potential harms across physical, financial, and digital dimensions |
| **Research Maturity** | Medium | ReAct (ICLR 2023) established foundations; 1,600+ annotated failure traces now available via MAST-Data |
| **Deployment Status** | Production | Claude Code, Devin, <EntityLink id="E218">OpenAI</EntityLink> Assistants in commercial use; enterprise adoption accelerating |
| **Scalability** | Uncertain | Performance gains plateau at longer time horizons; 32-hour tasks show humans outperforming AI 2:1 |
| **Dominance Probability** | 25-40% | Strong growth trends but reliability constraints may limit ceiling |


## Key Links

| Source | Link |
|--------|------|
| Official Website | [lilianweng.github.io](https://lilianweng.github.io/posts/2023-06-23-agent/) |
| Wikipedia | [en.wikipedia.org](https://en.wikipedia.org/wiki/Movable_scaffolding_system) |
| <EntityLink id="E538">LessWrong</EntityLink> | [lesswrong.com](https://www.lesswrong.com/posts/mAwxebLw3nYbDivmt/scaffolded-llms-less-obvious-concerns) |
| arXiv | [arxiv.org](https://arxiv.org/html/2510.25445v1) |


## Overview

Heavy scaffolding refers to AI systems where significant capability and behavior emerges from the **orchestration code** rather than just the underlying model. These systems combine foundation models with tools, persistent memory, multi-agent coordination, and autonomous operation loops.

Examples include Claude Code (<EntityLink id="E22">Anthropic</EntityLink>'s coding agent), Devin (Cognition's software engineer), AutoGPT, and various research agent frameworks. The key distinguishing feature is that the **scaffold itself is a major determinant of system behavior**, not just a thin wrapper around model calls.

This paradigm has an estimated **25-40% probability** of being dominant at transformative AI, with strong growth trends as scaffolding becomes easier to build and demonstrates clear capability gains. The [2025 International AI Safety Report](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2025) notes that "increasingly capable AI agents will likely present new, significant challenges for risk management."

## Agentic Architecture Patterns

The following diagram illustrates the common architectural patterns found in modern agentic systems, showing how different components interact across the planning, execution, and feedback loops:

<Mermaid chart={`
flowchart TD
    subgraph input["User Input"]
        task["Task/Goal"]
    end

    subgraph planning["Planning Layer"]
        decompose["Task Decomposition"]
        select["Strategy Selection"]
        schedule["Action Scheduling"]
    end

    subgraph execution["Execution Layer"]
        toolcall["Tool Invocation"]
        observe["Observation Processing"]
        reflect["Self-Reflection"]
    end

    subgraph memory["Memory Systems"]
        short["Short-term Context"]
        long["Long-term Storage"]
        episodic["Episodic Memory"]
    end

    subgraph external["External Environment"]
        apis["APIs/Web"]
        files["File Systems"]
        code["Code Execution"]
        browser["Browser"]
    end

    task --> decompose
    decompose --> select
    select --> schedule
    schedule --> toolcall
    toolcall --> apis
    toolcall --> files
    toolcall --> code
    toolcall --> browser
    apis --> observe
    files --> observe
    code --> observe
    browser --> observe
    observe --> reflect
    reflect --> short
    short --> long
    long --> episodic
    episodic --> decompose
    reflect --> schedule

    style planning fill:#e8f4f8
    style execution fill:#f0f8e8
    style memory fill:#f8f0e8
    style external fill:#f8e8e8
`} />

## Conceptual Architecture

<Mermaid chart={`
flowchart TB
    subgraph scaffold["Scaffold Layer (Readable Code)"]
        orchestrator["Orchestrator"]
        memory["Persistent Memory"]
        tools["Tool Registry"]
        planner["Planning Module"]
        critic["Self-Critique"]
    end

    subgraph models["Model Layer (Black Box)"]
        llm1["LLM: Reasoning"]
        llm2["LLM: Coding"]
        llm3["LLM: Critique"]
    end

    subgraph external["External World"]
        files["File System"]
        web["Web/APIs"]
        exec["Code Execution"]
    end

    orchestrator --> planner
    planner --> llm1
    llm1 --> critic
    critic --> llm3
    orchestrator --> tools
    tools --> llm2
    llm2 --> exec
    tools --> web
    memory --> orchestrator
    exec --> files
`} />

## Key Properties

| Property | Rating | Assessment |
|----------|--------|------------|
| **White-box Access** | MEDIUM-HIGH | Scaffold code is fully readable and auditable; model calls remain black boxes |
| **Trainability** | LOW | Models trained separately; scaffold is engineered code, not learned |
| **Predictability** | LOW | Multi-step plans can diverge unpredictably; emergent behavior from agent loops |
| **Modularity** | HIGH | Explicit component architecture with clear boundaries |
| **Formal Verifiability** | PARTIAL | Scaffold logic can be formally verified; model outputs cannot |

## Safety Implications

### Advantages

| Advantage | Explanation |
|-----------|-------------|
| **Auditable orchestration** | Every decision point in the scaffold can be logged, reviewed, and understood |
| **Insertable safety checks** | Can add human approval, sandboxing, or constraint checking in code |
| **Modular failure isolation** | When something breaks, you can identify which component failed |
| **Testable control flow** | Can write unit tests for scaffold behavior, even if model outputs vary |
| **Interpretable planning** | Multi-step plans are often explicitly represented and inspectable |

### Risks

| Risk | Severity | Explanation | Mitigation Status |
|------|----------|-------------|-------------------|
| **Emergent multi-step behavior** | HIGH | Behavior emerges from interaction of components over many steps; 14 failure modes identified in MAST taxonomy | Active research; 14% improvement achievable with interventions |
| **Autonomous operation** | HIGH | Less human oversight when agents run for extended periods; agents now run 30+ hours autonomously | Human-in-loop checkpoints being standardized |
| **Tool use enables real harm** | HIGH | File system, web access, code execution = real-world consequences | Sandboxing, permission systems widely deployed |
| **Deceptive scaffolding** | MEDIUM | Scaffold could be designed (or evolve) to hide intentions | Audit trails, logging standard practice |
| **Scaling unpredictability** | MEDIUM | More agents, longer loops = harder to predict outcomes | [Gradient Institute](https://www.gradientinstitute.org/assets/gradient_multiagent_report.pdf) developing risk analysis techniques |
| **Security vulnerabilities** | HIGH | [Survey](https://arxiv.org/abs/2510.23883) identifies 4 knowledge gaps: unpredictable inputs, complex execution, variable environments, untrusted entities | Emerging field; defenses lagging threats |

## Current Examples

| System | Developer | Key Features | Benchmark Performance | Status |
|--------|-----------|--------------|----------------------|--------|
| **[Claude Code](https://www.anthropic.com/news/claude-sonnet-4-5)** | Anthropic | Coding agent with file access, terminal, multi-file editing | 77.2% SWE-bench Verified | Production |
| **[Devin](https://www.cognition.ai/)** | Cognition | Full software engineer agent with browser, terminal | First to reach 13.86% SWE-bench (Mar 2024); valued at \$10.2B | Production |
| **[CUGA](https://research.ibm.com/blog/cuga-agent-framework)** | IBM Research | Enterprise-ready hierarchical planner-executor | 61.7% WebArena (SOTA) | Production |
| **[AutoGPT](https://github.com/significant-gravitas/AutoGPT)** | Open source | General autonomous agent with plugins | 181K+ GitHub stars | Research/Hobby |
| **[MetaGPT](https://github.com/geekan/MetaGPT)** | Open source | Multi-agent framework with SOPs | 83%+ on HumanEval | Framework |
| **[Voyager](https://voyager.minedojo.org/)** | NVIDIA | Minecraft agent with skill library | First LLM-powered embodied agent | Research |
| **[OpenAI Assistants](https://platform.openai.com/docs/assistants)** | OpenAI | API for building custom agents with tools | Code Interpreter, retrieval | Production |
| **[LangChain Agents](https://python.langchain.com/)** | LangChain | Framework for building agent pipelines | 140M+ monthly downloads | Framework |

## Benchmark Performance Data

Empirical benchmarks provide quantitative evidence of agentic system capabilities and limitations. The table below summarizes performance across major evaluation suites:

| Benchmark | Task Type | Best Agent Performance | Human Baseline | Key Finding |
|-----------|-----------|----------------------|----------------|-------------|
| **[SWE-bench Verified](https://www.swebench.com/)** | Software engineering | 77.2% ([Claude Sonnet 4.5](https://www.anthropic.com/news/claude-sonnet-4-5)); 80.9% (Opus 4.5) | ≈90% (estimated) | 5.5x improvement from 13.86% (Devin, Mar 2024) to 77.2% (Sep 2025) |
| **[SWE-bench Pro](https://www.swebench.com/)** | Complex software tasks | 23.3% (GPT-5/Claude Opus 4.1) | Not measured | Significant drop from Verified; highlights reliability gap |
| **[WebArena](https://webarena.dev/)** | Web navigation | 61.7% ([IBM CUGA](https://research.ibm.com/blog/cuga-agent-framework), Feb 2025) | 78.24% | 4.3x improvement from 14.41% baseline (2023); [Zhou et al. 2023](https://arxiv.org/abs/2307.13854) |
| **WebChoreArena** | Tedious web tasks | 37.8% (Gemini 2.5 Pro) | Not measured | Memory and calculation tasks remain challenging |
| **[ALFWorld](https://alfworld.github.io/)** | Embodied tasks | 48.5% (GPT-4 AutoGPT) | ≈95% | Surpassed imitation learning baselines; [Liu et al. 2023](https://arxiv.org/abs/2306.02224) |
| **[HotPotQA](https://hotpotqa.github.io/)** | Multi-hop QA | 27.4% (ReAct) | ≈60% | ReAct trails CoT slightly but gains interpretability; [Yao et al. 2022](https://arxiv.org/abs/2210.03629) |
| **[RE-Bench](https://metr.org/)** | Complex tasks (2hr) | 4x human score | Baseline | At 32 hours, humans outperform AI 2:1; time-horizon dependent |
| **[AppWorld](https://arxiv.org/abs/2403.19953)** | API orchestration | 48.2% (IBM CUGA) | Not measured | 87.5% on Level 1 tasks; complex multi-API coordination |

### Performance Trends

The trajectory of agentic systems shows rapid improvement but persistent reliability gaps:

| Metric | 2023 | 2024 | 2025 | Trend |
|--------|------|------|------|-------|
| SWE-bench (best agent) | 13.86% (Devin) | 49% (Claude 3.5 Sonnet) | 77.2% (Claude Sonnet 4.5) | +463% over 2 years |
| WebArena success rate | 14.41% | ≈45% | 61.7% | +328% over 2 years |
| Multi-agent task completion | 35-40% | 45-55% | 55-65% | Steady improvement |
| Error propagation rate | High (unmeasured) | ≈60% cascade failures | ≈45% with mitigations | Improving with research |

## Research Landscape

### Key Papers

| Paper | Year | Venue | Contribution | Key Metrics |
|-------|------|-------|--------------|-------------|
| [ReAct: Synergizing Reasoning and Acting](https://arxiv.org/abs/2210.03629) | 2022 | ICLR 2023 | Foundational reasoning+action framework | +34% absolute on ALFWorld; 94% fact accuracy |
| [Toolformer](https://arxiv.org/abs/2302.04761) | 2023 | NeurIPS | Self-supervised tool use learning | Models learn APIs from 25K demonstrations |
| [Voyager](https://voyager.minedojo.org/) | 2023 | NeurIPS | First LLM-powered embodied agent | 3.3x more unique items discovered vs baselines |
| [Generative Agents](https://arxiv.org/abs/2304.03442) | 2023 | UIST | Believable simulacra with memory | 25 agents; 2-week simulated time |
| [AgentVerse](https://arxiv.org/abs/2308.10848) | 2024 | ICLR 2024 | Multi-agent collaboration framework | Meta-programming; dynamic role adjustment |
| [SWE-bench](https://arxiv.org/abs/2310.06770) | 2023 | ICLR 2024 | Real GitHub issue resolution benchmark | 2,294 tasks from 12 popular repositories |
| [MAST-Data](https://arxiv.org/abs/2503.13657) | 2025 | arXiv | Multi-agent failure taxonomy | 1,600+ traces; 14 modes; κ=0.88 agreement |
| [Agentic AI Security](https://arxiv.org/abs/2510.23883) | 2025 | arXiv | Security threat taxonomy | 4 knowledge gaps; comprehensive defense survey |

### Multi-Agent Failure Taxonomy

Research from the [MAST-Data study](https://arxiv.org/abs/2503.13657) identifies 14 unique failure modes clustered into three categories:

| Category | Failure Modes | Frequency | Mitigation |
|----------|--------------|-----------|------------|
| **System Design Issues** | Improper task decomposition, inadequate tool selection, memory overflow | 35-40% of failures | Better planning modules, explicit verification |
| **Inter-Agent Misalignment** | Conflicting objectives, communication breakdowns, role confusion | 25-30% of failures | Standardized protocols, centralized coordination |
| **Task Verification** | Incomplete outputs, quality control failures, premature termination | 30-35% of failures | Human-in-loop checkpoints, automated testing |

The study found inter-annotator agreement (kappa = 0.88) validating the taxonomy, and that interventions yielded +14% improvement for ChatDev but "remain insufficiently [high] for real-world deployment."

### Key Labs and Organizations

| Organization | Focus Area | Key Achievements | Notable Systems |
|--------------|------------|------------------|-----------------|
| **[Anthropic](https://www.anthropic.com/)** | Frontier agents + safety | 77.2% SWE-bench; 30+ hour sustained operation | Claude Code, Computer Use |
| **[Cognition](https://www.cognition.ai/)** | Autonomous software engineering | First 13.86% SWE-bench (Mar 2024); \$10.2B valuation | Devin |
| **[OpenAI](https://openai.com/)** | Agent APIs + reasoning | Code Interpreter, function calling ecosystem | Assistants API, o1/o3 reasoning |
| **[IBM Research](https://research.ibm.com/)** | Enterprise-ready agents | 61.7% WebArena SOTA (Feb 2025); open-source | [CUGA](https://research.ibm.com/blog/cuga-agent-framework) |
| **[LangChain](https://www.langchain.com/)** | Agent frameworks | 140M+ monthly PyPI downloads | LangGraph, LangSmith |
| **[MetaGPT](https://github.com/geekan/MetaGPT)** | Multi-agent SOPs | 47K+ GitHub stars; standardized workflows | MetaGPT framework |
| **[NVIDIA](https://www.nvidia.com/)** | Embodied agents | First LLM-powered embodied agent | [Voyager](https://voyager.minedojo.org/) |

## Trend Analysis

Heavy scaffolding is experiencing rapid growth due to several factors:

1. **Scaffolding is getting cheaper** - Frameworks like LangChain, LlamaIndex, MetaGPT reduce development time by 60-80%
2. **Clear capability gains** - Agents demonstrably outperform single-turn interactions; SWE-bench improved 5.5x in two years
3. **Tool use is mature** - Function calling, code execution are well-understood; 90%+ of production agents use tool calling
4. **Enterprise demand** - [McKinsey reports](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/deploying-agentic-ai-with-safety-and-security-a-playbook-for-technology-leaders) agentic AI adds "additional dimension to the risk landscape" as systems move from enabling interactions to driving transactions

### Enterprise Adoption Landscape

| Metric | 2024 | 2025 | Change | Source |
|--------|------|------|--------|--------|
| Fortune 500 production deployments | 19% | 67% | +248% YoY | [Axis Intelligence](https://axis-intelligence.com/agentic-ai-statistics-2025-2026/) |
| Organizations using Microsoft Copilot Studio | — | 230,000+ | Including 90% of Fortune 500 | [Kong Inc. Report](https://konghq.com/company/press-room/press-release/agentic-ai-report) |
| Fortune 100 using AutoGen framework | — | 40%+ | For internal agentic systems | [Microsoft Research](https://www.microsoft.com/en-us/research/) |
| Full trust in AI agents for core processes | — | 6% | 43% trust for limited tasks only | [HBR Survey 2025](https://fortune.com/2025/12/09/harvard-business-review-survey-only-6-percent-companies-trust-ai-agents/) |
| Gartner projection: Enterprise software with agentic AI | less than 1% | 33% by 2028 | 33x growth projected | [Gartner](https://www.gartner.com/) |

**Trust Gap Analysis:** While [90% of enterprises report actively adopting AI agents](https://konghq.com/company/press-room/press-release/agentic-ai-report), only 6% express full trust for core business processes. 43% trust agents only for limited/routine operational tasks, and 39% restrict them to supervised use cases. This trust gap represents both a current limitation and an opportunity for safety-focused development.

### Trajectory Projection

| Period | Expected Development | Confidence |
|--------|---------------------|------------|
| 2024-2025 | Specialized vertical agents (coding, research, customer service) | High (already occurring) |
| 2025-2027 | General-purpose agents with longer autonomy; 70%+ benchmark performance | Medium-High |
| 2027-2030 | Multi-agent ecosystems, agent-to-agent collaboration | Medium |
| 2030+ | Potential dominant paradigm if reliability exceeds 90% | Low-Medium |

### Growth Indicators

| Metric | Value | Source |
|--------|-------|--------|
| GitHub stars (AutoGPT) | 181,000+ | [GitHub Repository](https://github.com/significant-gravitas/AutoGPT) |
| Agent framework downloads/month | 140M+ (LangChain) | [PyPI Stats](https://pypistats.org/packages/langchain) |
| Enterprise agent deployments | 67% of Fortune 500 in production | [Axis Intelligence 2025](https://axis-intelligence.com/agentic-ai-statistics-2025-2026/) |
| AI startup funding (2025) | \$202B total, 50% of all VC | [Crunchbase 2025](https://news.crunchbase.com/ai/big-funding-trends-charts-eoy-2025/) |
| Agent-related papers (2024) | 500+ on arXiv | [Awesome-Agent-Papers](https://github.com/luo-junyu/Awesome-Agent-Papers) |
| Agentic AI market projection | \$89.6B by 2026 | [DigitalDefynd 2025](https://digitaldefynd.com/IQ/agentic-ai-statistics/) |

## Cost and Economics

Understanding the economics of agentic systems is critical for both deployment decisions and safety considerations.

### API and Compute Costs

| Model/System | Input Cost | Output Cost | Context Window | Typical Task Cost |
|--------------|-----------|-------------|----------------|-------------------|
| Claude Sonnet 4.5 | \$3/M tokens | \$15/M tokens | 200K tokens | \$0.50-5.00 per SWE-bench task |
| GPT-4o | \$2.50/M tokens | \$10/M tokens | 128K tokens | \$0.30-3.00 per task |
| Claude Opus 4.5 | \$15/M tokens | \$75/M tokens | 200K tokens | \$2.00-20.00 per complex task |
| Open-source (Llama 3.1 70B) | ≈\$0.50/M tokens | ≈\$0.75/M tokens | 128K tokens | \$0.10-1.00 per task |

### Cost-Benefit Analysis

| Metric | Value | Source |
|--------|-------|--------|
| Average agent task cost (coding) | \$0.50-5.00 | API pricing estimates |
| Human developer hourly rate | \$75-200/hour | Industry averages |
| Break-even threshold | Agent 3-4x slower than human | Cost parity analysis |
| Enterprise ROI on agent deployment | 2-5x within first year | [McKinsey 2025](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/deploying-agentic-ai-with-safety-and-security-a-playbook-for-technology-leaders) |
| Venture funding in AI agents (2025) | \$202B total AI; agents dominate | [Crunchbase](https://news.crunchbase.com/ai/big-funding-trends-charts-eoy-2025/) |

## Comparison with Other Paradigms

| Aspect | Heavy Scaffolding | Minimal Scaffolding | Provable Systems |
|--------|-------------------|---------------------|------------------|
| Interpretability | Scaffold: HIGH, Model: LOW | LOW | HIGH by design |
| Capability ceiling | HIGH (tool use) | LIMITED | UNKNOWN |
| Development speed | FAST | FAST | SLOW |
| Safety guarantees | PARTIAL (scaffold only) | NONE | STRONG |
| Current maturity | MEDIUM | HIGH | LOW |

## Key Uncertainties

| Uncertainty | Current Evidence | Implications |
|-------------|------------------|--------------|
| **Reliability at scale** | RE-Bench shows humans outperform AI 2:1 at 32-hour tasks; error propagation causes 45-60% of failures | May limit agent autonomy to shorter task horizons (under 8 hours) |
| **Emergent deception** | [ACM survey](https://dl.acm.org/doi/10.1145/3716628) identifies "emergent behaviors" including "destructive behaviors leading to undesired outcomes" | Multi-agent coordination introduces unpredictability absent in single-agent systems |
| **Human oversight integration** | [Nature study](https://www.nature.com/articles/s41467-025-63913-1) proposes triadic framework: human regulation, agent alignment, environmental feedback | Current systems lack standardized oversight mechanisms |
| **Scaffold complexity** | Agent Workflow Memory achieved 51% success boost; architectural choices matter as much as model capability | Scaffold engineering may become a specialized discipline |
| **Error propagation** | Chain-of-Thought acts as "error amplifier" where minor mistakes cascade through subsequent actions | Early detection and correction are critical; memory and reflection reduce risk |

## Implications for Safety Research

### Research That Transfers Well

- **Control and containment** - Sandboxing, permission systems, action constraints
- **Interpretability of plans** - Understanding multi-step reasoning
- **Human-in-the-loop design** - Approval workflows, uncertainty communication
- **Testing and red-teaming** - Adversarial evaluation of agent systems

### Research That May Not Transfer

- **Mechanistic interpretability** - Scaffold behavior isn't in weights
- **Training-time interventions** - Scaffold isn't trained
- **Representation analysis** - Scaffold doesn't have representations

## Sources and Further Reading

### Primary Research
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) - Yao et al., ICLR 2023. Foundational paper establishing reasoning+action paradigm; 34% absolute improvement on ALFWorld.
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://arxiv.org/abs/2307.13854) - Zhou et al., 2023. Standard benchmark for web agents with 812 tasks.
- [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657) - Cemri et al., 2025. MAST-Data with 1,600+ annotated failure traces; 14 failure modes identified.
- [Agentic AI Security: Threats, Defenses, Evaluation](https://arxiv.org/abs/2510.23883) - Comprehensive security analysis identifying 4 knowledge gaps.
- [Towards Enterprise-Ready Computer Using Generalist Agent](https://arxiv.org/html/2503.01861v2) - IBM CUGA technical paper; 61.7% WebArena SOTA.
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) - Jimenez et al., 2023. Software engineering benchmark.

### Industry Reports
- [International AI Safety Report 2025](https://internationalaisafetyreport.org/publication/international-ai-safety-report-2025) - Multi-government assessment: "increasingly capable AI agents will likely present new, significant challenges for risk management."
- [McKinsey: Deploying Agentic AI with Safety and Security](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/deploying-agentic-ai-with-safety-and-security-a-playbook-for-technology-leaders) - Enterprise deployment playbook for technology leaders.
- [Anthropic Claude Sonnet 4.5 Technical Report](https://www.anthropic.com/news/claude-sonnet-4-5) - 77.2% SWE-bench Verified; 30+ hour sustained operation.
- [Kong Inc.: Agentic AI Report](https://konghq.com/company/press-room/press-release/agentic-ai-report) - 90% of enterprises actively adopting AI agents; 79% expect full-scale adoption within 3 years.
- [Crunchbase: AI Funding Trends 2025](https://news.crunchbase.com/ai/big-funding-trends-charts-eoy-2025/) - AI captured 50% of all global funding (\$202B total).

### Surveys and Collections
- [Awesome-Agent-Papers](https://github.com/luo-junyu/Awesome-Agent-Papers) - Curated collection of 500+ LLM agent papers.
- [LLM-Agents-Papers](https://github.com/AGI-Edgerunners/LLM-Agents-Papers) - Comprehensive repository of agent research.
- [ACM Computing Surveys: AI Agents Under Threat](https://dl.acm.org/doi/10.1145/3716628) - Security challenges survey; identifies "emergent behaviors" including "destructive behaviors."
- [Gradient Institute: Multi-Agent Risk Analysis](https://www.gradientinstitute.org/assets/gradient_multiagent_report.pdf) - Risk analysis techniques for multi-agent systems.