February 2026

The Autonomy Index

Tracking and scoring the global AI agent ecosystem across autonomy, security, ecosystem strength, and execution control.

Index Movement — Last 7 Days
AMI Agent Maturity
+2.4
Avg across 9 frameworks
ARI Autonomy Risk
−1.8
Risk declining sector-wide
EPI Ecosystem Power
+3.1
Driven by MCP adoption
Notable Shift
CrewAI sandbox update reduces ARI by 5 pts — largest single-week security improvement this quarter.
Premium Report (.html) Sources & Citations Raw Data (.csv)

5 Things You Need to Know Right Now

The critical developments shaping agent strategy in 2026 — each backed by verified data.

185K★

OpenClaw → OpenAI Pipeline Confirmed

Creator Peter Steinberger joins OpenAI. OpenClaw moves to independent foundation with OpenAI backing. 185K GitHub stars in 6 weeks — fastest open-source agent adoption ever recorded. But: 512 vulnerabilities found in January audit.

CONFIRMED — Reuters, Feb 15
72.7%

Claude Opus 4.6 Sets Agent Benchmark Record

72.7% on OSWorld (autonomous computer use), 80.8% SWE-bench, 65.4% Terminal-Bench. Anthropic now holds the highest published score for autonomous computer control — but lacks a first-party agent framework.

CONFIRMED — Anthropic, Feb 5
5%→40%

Enterprise Agent Adoption: 8× Growth in 2 Years

Gartner: 5% of enterprise apps used AI agents in 2024 → projected 40% by end of 2026. This isn't experimental anymore. Enterprises are deploying agents in production workflows at scale.

CONFIRMED — Gartner, Aug 2025
512

Agent Security Is Now a Real Crisis

OpenClaw: 512 vulnerabilities (Kaspersky), 40,214 exposed instances (SecurityScorecard), 3 published CVEs. The gap between agent capability and agent security is widening — and enterprises are noticing.

CONFIRMED — Kaspersky, Feb 2026
10K+

MCP Becomes the Agent Connectivity Standard

Anthropic's Model Context Protocol: 10,000+ public servers, adopted by ChatGPT, Cursor, Gemini, VS Code, Copilot. Now under Linux Foundation governance. The tool layer is standardizing — the orchestration layer is not.

CONFIRMED — Anthropic, Dec 2025

Key Metrics — With Baselines

Numbers without context are decoration. Here's what these actually mean.

$2.02T
Total AI Market 2026
Up from $184B in 2024 — 11× in 2 years
185K★
OpenClaw GitHub Stars
vs LangChain 100K, AutoGen 40K, CrewAI 25K
72.7%
Opus 4.6 OSWorld
Highest published autonomous computer use score
40,214
Exposed Agent Instances
OpenClaw instances found on public internet
40%
Enterprise Agent Adoption
Projected by EOY 2026 — up from 5% in 2024

Framework Intelligence — February 2026

Three indices measuring maturity, risk, and ecosystem power. Each score is decomposable, evidence-linked, and confidence-labeled.

Agent Framework Index Snapshot
v0.1 · Feb 2026 · 9 frameworks · 38 sources
Scores derived from 6-dimension weighted analysis. All methodology published. View full methodology →
AMI: Agent Maturity (0–100, higher = more mature)
ARI: Autonomy Risk (0–100, higher = more risk)
EPI: Ecosystem Power (0–100, higher = more reach)

Maturity vs. Risk — The Tradeoff Map

AMI (maturity) on X-axis vs ARI (risk) on Y-axis. Ideal position: bottom-right (mature + low risk). Size = EPI (ecosystem power).
Source: Autonomy Index proprietary scoring · Methodology

Interactive Data Visualizations

All data sourced from verified reports. Hover for details. Confidence levels noted per chart.

Enterprise AI Agent Adoption

Percentage of enterprise apps using AI agents, 2024–2027
Sources: Gartner (Aug 2025), Deloitte (2025) • CONFIRMED data points with projected trendline

Agent Framework Popularity

GitHub stars as of February 2026
Source: GitHub, Feb 2026 • All CONFIRMED

OpenClaw Star Velocity

GitHub stars growth over first 6 weeks (Jan–Feb 2026)
Sources: DigitalOcean, Adaptavist, LinkedIn • CONFIRMED

Security Vulnerability Comparison

Known vulnerabilities and exposed instances, Feb 2026
Sources: Kaspersky, SecurityScorecard, source code inspection • CONFIRMED

LLM Pricing: Input vs Output Cost

Cost per million tokens, Feb 2026
Sources: Anthropic, OpenAI official pricing • GPT-5 and Gemini 3.0 INFERRED

Competitive Position Radar

Multi-axis comparison across 6 dimensions (1–5 scale)
Source: Report analysis • Security scores CONFIRMED, others INFERRED

Where Every Player Sits

Two visualizations: a strategic 2×2 matrix and a full ecosystem map.

Strategic Positioning Matrix

Cloud-Native ↔ Local-First × Workflow Builder ↔ Autonomous Agent
← Workflow Builder                   Autonomous Agent →

☁️ Cloud + Autonomous

  • OpenClaw (185K★)
  • OpenAI Agents SDK
  • Devin (Cognition)
  • GitHub Copilot Workspace

🖥️ Local + Autonomous

  • Clawdia (Anthropic-native)
  • Aider (terminal agent)
  • Continue.dev
  • Local LLM + shell agents

☁️ Cloud + Workflow

  • n8n / Make / Zapier
  • LangChain / LangGraph
  • Dify (60K★)
  • Vercel AI SDK

🖥️ Local + Workflow

  • Haystack (Deepset)
  • Semantic Kernel (MSFT)
  • AutoGen (local mode)
  • PydanticAI
← Cloud-Native                                         Local-First →

Agent Ecosystem Map — February 2026

Key players organized by alignment and category

OpenAI Orbit

  • OpenClaw (foundation)
  • Agents SDK
  • Operator
  • GPT Actions
  • Assistants API v2

Anthropic Orbit

  • Claude Computer Use
  • MCP (Linux Foundation)
  • Claude Connectors (75+)
  • Clawdia Project to Watch
  • Tool Use API

Google Orbit

  • Gemini 3.0 Agents
  • Vertex AI Agent Builder
  • Project Mariner
  • Firebase Genkit
  • A2A Protocol

Open Source

  • LangChain (100K★)
  • AutoGen (40K★)
  • CrewAI (25K★)
  • Haystack (18K★)
  • PydanticAI (new)

Enterprise

  • ServiceNow (Claude)
  • Salesforce Agentforce
  • Semantic Kernel (25K★)
  • AWS Bedrock Agents
  • Snowflake + Claude

Agent Frameworks: Head-to-Head

9 frameworks scored across 6 radar dimensions + 3 proprietary indices. Click any row to expand intelligence detail.

Framework AMI ARI EPI Reasoning Ecosystem Security Autonomy
Click any row to expand evidence, risks, and recommended use-case View full methodology →

3 Agent Playbooks — Step by Step

Choose your architecture. Each approach has different tradeoffs for security, speed, and control.

Build a Cloud-Aligned Agent (OpenClaw Architecture)

# 1. Setup — Install and configure npm install -g @openclaw/cli openclaw init my-agent cd my-agent # 2. Configure LLM backend export OPENAI_API_KEY="sk-..." openclaw config set model "gpt-4.1" # 3. Define agent capabilities openclaw add-tool filesystem # Read/write files openclaw add-tool terminal # Execute shell commands openclaw add-tool browser # Web browsing # 4. Run autonomous task openclaw run "Refactor the auth module to use JWT tokens" # 5. Review and approve changes openclaw diff # See proposed changes openclaw apply # Apply approved changes
✓ Strengths
  • Fastest setup (minutes)
  • Large community (185K★)
  • Multi-LLM backend support
  • Browser-based — no desktop app needed
  • Active development velocity
✗ Risks
  • 512 known vulnerabilities (Jan 2026)
  • 40K+ exposed instances on public internet
  • Code leaves your machine by default
  • Foundation governance still untested
  • Creator now employed by OpenAI

Build a Claude-Based Reasoning Agent

# Claude agent with tool use — Python import anthropic client = anthropic.Anthropic() # Define tools the agent can use tools = [ { "name": "read_file", "description": "Read contents of a file", "input_schema": { "type": "object", "properties": { "path": {"type": "string"} } } }, { "name": "write_file", "description": "Write content to a file", "input_schema": { "type": "object", "properties": { "path": {"type": "string"}, "content": {"type": "string"} } } } ] # Agentic loop — Claude reasons, calls tools, repeats messages = [{"role": "user", "content": "Audit auth.py for security issues and fix them"}] while True: response = client.messages.create( model="claude-opus-4-6-20260205", max_tokens=8192, tools=tools, messages=messages ) # Process tool calls if response.stop_reason == "tool_use": for block in response.content: if block.type == "tool_use": result = execute_tool(block.name, block.input) messages.append({"role": "assistant", "content": response.content}) messages.append({"role": "user", "content": [{ "type": "tool_result", "tool_use_id": block.id, "content": result }]}) else: break # Agent finished reasoning
✓ Strengths
  • Best reasoning depth (Opus 4.6)
  • 200K context window for large codebases
  • Full control over tool permissions
  • MCP integration for 10K+ tool servers
  • Enterprise-grade safety alignment
✗ Risks
  • No first-party agent framework
  • Must build orchestration yourself
  • Higher API cost ($5/$25 per M tokens)
  • Smaller agent community vs OpenAI
  • Memory/persistence requires external layer

Build an Autonomous Workflow in Clawdia (Local-First)

# 1. Clone and install git clone https://github.com/chillysbabybackribs/Clawdia.git cd Clawdia pnpm install # 2. Configure — add your Anthropic API key # Launch the desktop app pnpm dev # 3. Clawdia operates with OS-level access: # → File system (read/write/delete) # → Shell execution (any command) # → Browser automation (navigate, extract, interact) # → Process management (list, kill, monitor) # → Task scheduling (cron-based persistent tasks) # 4. Example autonomous objective: # "Audit all JavaScript files in ~/projects/webapp for # security vulnerabilities, create a report, and fix # critical issues with git commits for each fix" # 5. Security model: # → All execution is LOCAL (no cloud dependency) # → Encrypted config store (electron-store) # → Tool-level permission boundaries # → Audit trail via conversation history # → No data leaves your machine (except API calls) # 6. Persistent task example: # "Every morning at 9am, check my GitHub repos for # new issues, summarize them, and draft responses"
✓ Strengths
  • Zero exposed attack surface
  • Full OS-level integration
  • Opus 4.6 reasoning (highest benchmarks)
  • Persistent scheduled tasks (cron)
  • Data never leaves your machine
✗ Risks
  • Early stage (1 star, single developer)
  • Anthropic-only (no multi-LLM support)
  • Desktop-only (no cloud/team mode)
  • Requires Electron + Node.js setup
  • No marketplace or plugin ecosystem yet

Which Approach Fits Your Profile?

Enterprise vs. solo founder. Cloud vs. local. Speed vs. security. Here's the decision framework.

🏢 Enterprise Teams (50+ developers)

  • Start with LangChain/LangGraph for framework maturity
  • Evaluate ServiceNow + Claude for workflow agents
  • Use MCP for standardized tool connectivity
  • Mandate security audits before deploying any agent
  • Avoid OpenClaw in production until security improves

🚀 Startups (5–50 developers)

  • Vercel AI SDK + Claude for product-facing agents
  • CrewAI for multi-agent task orchestration
  • OpenClaw for rapid prototyping (not production)
  • Build on MCP — it's becoming the standard
  • Evaluate Clawdia for internal dev tooling

👤 Solo Founders & Indie Hackers

  • Clawdia for personal autonomous workflows
  • Claude API directly for custom agents
  • n8n for no-code automation bridges
  • Focus on agents that save >10 hours/week
  • Don't over-invest in frameworks — ship fast

🔒 Security-Sensitive Environments

  • Local-first architectures only (Clawdia model)
  • Air-gapped LLM inference where possible
  • Audit all agent tool permissions pre-deploy
  • Never expose agent instances to public internet
  • Implement kill switches and rate limiting