AI agents went from research curiosity to production reality in 2025. Claude Code hit $1 billion in annual run rate within six months, capturing 54% of the coding market. Cursor crossed 2 million users. Devin attracted $2 billion in funding. But which agent is right for your use case? They differ in underlying models, tool integrations, memory systems, and planning capabilities—differences that matter enormously for production deployment.
NeoSignal Agents category showing scored agent systems with capability metrics
NeoSignal Agents provides the comparison framework. Claude Code leads at 92, with agent-specific metrics: commercial license, Anthropic provider, coding agent type, 25 tool integrations. OpenAI CUA follows at 91 for computer use tasks. Cursor shows 90 for IDE-integrated coding. Each card reveals the metrics that matter: license type, agent specialization, provider ecosystem, and compatibility with foundation models.
The benefit: understand agent capabilities before building your workflow around them. The chat panel offers agent-specific guidance—"What are the top agent frameworks in 2025?" gets contextual answers based on current market data.
Detailed Walkthrough
The Agent Explosion
2025 saw AI agents transition from demos to production:
- Claude Code: Anthropic's coding agent achieved $1B run rate in 6 months
- Cursor: IDE agent crossed 2M users and $100M ARR
- Devin: Cognition's autonomous software engineer raised $2B
- OpenAI CUA: Computer Use Agent expanded beyond browsing
- Cline: Open-source coding agent gained rapid adoption
But agents aren't interchangeable. Claude Code excels at codebase understanding; Cursor integrates tightly with VS Code; Devin handles multi-step software projects autonomously. Choosing the wrong agent for your use case wastes money and delivers poor results.
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Agent Category Overview
The Agents page displays all indexed agent systems:
Agent Cards show:
- Agent name and logo
- NeoSignal score (0-100) with trend indicator
- Agents category tag (pink)
- License type (commercial, Apache-2.0, MIT, research)
- Provider organization
- Agent type (coding, browser, general_assistant, clinical, multi_agent)
- Key metrics (valuation, run_rate for commercial agents)
Searchable: Filter by name, type, or provider. Type "coding" to see coding agents; type "open source" to see non-commercial options.
Score Ordering: Agents sorted by score. Claude Code (92), OpenAI CUA (91), Cursor (90), Devin (89), Cline (88) lead the ranking.
Agent-Specific Metrics
Agents have unique characteristics compared to models or hardware:
License: Commercial (paid subscription), Apache-2.0 (open source), MIT (open source), Research (academic use)
Provider: The organization that builds and maintains the agent
Agent Type: Specialization area
coding: Software development tasksbrowser: Web navigation and automationgeneral_assistant: Multi-purpose assistanceclinical: Healthcare applicationsmulti_agent: Orchestrating multiple agents
Tool Integrations Count: Number of supported tool types (file editing, terminal, browser, API calls)
Base Model Requirements: Foundation models the agent depends on
Run Rate: Annual revenue for commercial agents (when available)
Agent Detail Pages
Click an agent card for comprehensive detail:
Claude Code Example:
- Score: 92 with rising trend
- Subtitle: "$1B run rate in 6 months, 54% coding market share"
- Description: "Anthropic-powered autonomous coding agent. Strong tool use and planning and memory and self-correction capabilities."
Metrics Panel:
| Metric | Value |
|---|---|
| Provider | Anthropic |
| License | commercial |
| Agent Type | coding |
| Tool Integrations Count | 25 |
| Base Model Requirements | claude-sonnet-4, claude-opus-4 |
| Run Rate | $1,000,000,000 |
| Time To Run Rate | 6 months |
| Coding Market Share | 54% |
Score Breakdown for agents uses different dimensions than models:
| Dimension | Weight | Score | Description |
|---|---|---|---|
| Adoption | 15% | 90 | Community usage, market traction, ecosystem maturity |
| Tool Use | 25% | 95 | Effectiveness at invoking and using external tools |
| Memory Context | 20% | 90 | Ability to maintain context across interactions |
| Self Reflection | 15% | 88 | Capacity for error correction and improvement |
| Planning Reasoning | 25% | 94 | Multi-step planning and logical reasoning |
Compatibility shows which models work well with the agent:
- Claude Sonnet: 98% (same ecosystem)
- LangChain: 85% (framework integration)
Sources: Links to official documentation and GitHub repositories
Agent Type Deep Dive
Coding Agents (Claude Code, Cursor, Devin, Cline):
- Specialize in code generation, debugging, and refactoring
- Integrate with IDEs and terminals
- Understand codebase structure and dependencies
- Key metric: Tool Use score determines effectiveness
Browser Agents (OpenAI CUA, Manus AI):
- Navigate and interact with web interfaces
- Fill forms, extract data, automate workflows
- Handle dynamic content and authentication
- Key metric: Planning Reasoning for multi-step tasks
General Assistants (Writer Action Agent, Perplexity):
- Handle diverse tasks without specialization
- Strong at research, summarization, analysis
- Flexible tool integration
- Key metric: Adoption reflects real-world utility
Multi-Agent Systems (CrewAI):
- Orchestrate multiple specialized agents
- Handle complex workflows with role assignment
- Coordinate between agents for sub-tasks
- Key metric: Planning Reasoning for orchestration
Comparing Agents
When evaluating agents, consider:
License Requirements:
- Enterprise deployment often requires commercial licenses
- Open-source agents (Cline, CrewAI) offer customization freedom
- Research licenses restrict commercial use
Model Dependencies:
- Claude Code requires Claude models (Anthropic ecosystem lock-in)
- Cursor works with multiple providers (more flexibility)
- Consider model costs alongside agent subscription
Integration Complexity:
- IDE agents (Cursor) require minimal setup
- API agents need infrastructure integration
- Multi-agent systems require orchestration design
Use Case Fit:
- Coding: Claude Code or Cursor for daily development
- Automation: OpenAI CUA or Manus AI for web tasks
- Research: Perplexity for information synthesis
- Clinical: Abridge for healthcare-specific needs
Chat Integration
The Agents section integrates with NeoSignal AI chat:
- "What are Claude Code's key agent capabilities?" — Detailed capability breakdown
- "How does Claude Code compare to other agent frameworks?" — Comparative analysis
- "Best practices for building production agents" — Architectural guidance
- "Which agent benchmarks should I consider?" — Evaluation methodology
- "What models work best for agentic tasks?" — Foundation model recommendations
The chat understands agent context. From an agent detail page, ask specific questions about that agent's strengths and limitations.
Real-World Selection Scenarios
Software Team Augmentation: Your team wants AI coding assistance. Claude Code offers the strongest tool use (95) and planning (94), but requires commercial licensing. Cline offers similar capabilities with open-source flexibility (Apache-2.0) but slightly lower scores. Weigh cost vs. capability.
Web Automation Platform: Building automated web workflows. OpenAI CUA leads browser agents at 91, with strong computer use capabilities. Manus AI at 88 offers an alternative with general assistant flexibility. Consider scale and pricing.
Enterprise Deployment: Large organization with compliance requirements. Commercial agents (Claude Code, Cursor) provide support and SLAs. Open-source (Cline) offers auditability but requires internal expertise. Adoption scores indicate community validation.
Research and Experimentation: Exploring agent capabilities without commitment. IBM CUGA (research license) enables academic work. CrewAI (MIT license) allows multi-agent experimentation. Lower scores acceptable when learning.
The Agent Ecosystem
Agents don't exist in isolation. They connect to:
Foundation Models: Claude Code depends on Claude; model improvements directly enhance agent performance.
Frameworks: LangChain and LlamaIndex provide orchestration; compatibility scores indicate integration quality.
Tools: File systems, terminals, browsers, APIs—tool integration count measures agent versatility.
Cloud Providers: Deployment options vary; some agents run locally, others require cloud inference.
NeoSignal's Stack Builder lets you combine agents with other infrastructure components and validate compatibility before building workflows.
From Comparison to Deployment
The agent landscape evolves weekly. New agents launch, existing agents gain capabilities, market positions shift. NeoSignal Agents provides a stable framework for comparison—consistent metrics, updated scores, comprehensive profiles.
Use it to shortlist candidates, understand capability tradeoffs, and make informed decisions about which agents to integrate into your workflows. The goal isn't picking the "best" agent—it's picking the right agent for your specific use case, constraints, and ecosystem.
Agents joins Models, Accelerators, Cloud, and Frameworks as the fifth pillar of NeoSignal's infrastructure intelligence. Understanding who's building AI agents and how they perform is essential for anyone building on the agentic future.