This section introduces advanced techniques that extend AI agent capabilities beyond core patterns. These capabilities address production challenges such as long-term reliability, cross-agent collaboration, and the limitations of finite context windows.
The four main areas of advanced capabilities are:
ChatHistorySummarizationReducer, the scratchpad pattern for persistent session notes, and handling failure modes like poisoning or distraction 12-context-engineering/README.md13-32Browser-Use library with Playwright and vision-based reasoning 15-browser-use/README.md1-6Advanced capabilities provide the infrastructure that enables core design patterns to scale for production use.
Diagram: Advanced capabilities as infrastructure for core patterns
Sources: 11-agentic-protocols/README.md7-18 12-context-engineering/README.md33-58 13-agent-memory/README.md42-55 15-browser-use/README.md11-14
The following diagram maps high-level capabilities to specific code entities and libraries used in the repository.
Diagram: Technical mapping of capabilities to codebase entities
Sources: 11-agentic-protocols/README.md33-50 11-agentic-protocols/README.md87-102 12-context-engineering/README.md79-96 13-agent-memory/README.md20-25 14-microsoft-agent-framework/README.md137-145 15-browser-use/README.md58-67
Protocols provide the "universal adapter" for agents. MCP enables dynamic tool discovery, allowing agents to query a server for capabilities rather than having tools hard-coded 11-agentic-protocols/README.md53-56 A2A uses Agent Cards and Artifacts to allow agents from different vendors to collaborate on tasks 11-agentic-protocols/README.md87-102 NLWeb allows agents to discover and interact with website content via natural language interfaces 11-agentic-protocols/README.md17-18 For details, see Agentic Protocols.
While prompt engineering focuses on static instructions, Context Engineering manages the dynamic flow of information 12-context-engineering/README.md37-40 Key strategies include Agent Scratchpads for session-specific notes 12-context-engineering/README.md79-81 and Chat History Reduction (summarization or trimming) to stay within model token limits 12-context-engineering/README.md85-87 For details, see Context Engineering.
Memory transforms stateless applications into self-improving systems 13-agent-memory/README.md4-6 This includes Short-Term Memory via AgentSession for active context 13-agent-memory/README.md68-72 and Long-Term Memory for user preferences and facts that persist across sessions 13-agent-memory/README.md78-82 Advanced implementations like Cognee use knowledge graphs and vector embeddings for intelligent retrieval 13-agent-memory/README.md24-25
For details, see Agent Memory Systems.
Computer Use Agents (CUA) go beyond APIs to interact with the web as a human would 15-browser-use/README.md1-3 These agents use vision-enabled reasoning (e.g., Azure OpenAI GPT-4 Vision) to "see" page layouts and extract data 15-browser-use/15-browser-user.ipynb58-62 The workflow often involves a hybrid of an Agent for dynamic navigation and an Actor for precise, predictable control 15-browser-use/README.md71-80 For details, see Browser Use Agents.
| Capability | Primary Purpose | Key Code Symbols / Tools |
|---|---|---|
| MCP | Dynamic Tool Access | MCP Client, MCP Server |
| A2A | Cross-Agent Collaboration | AgentCard, Artifact, AgentExecutor |
| Context Management | Window Optimization | ChatHistorySummarizationReducer, Scratchpad |
| Memory | Long-term Persistence | Mem0, Cognee, AgentSession |
| Browser Use | UI Interaction | Browser-Use, Playwright, CDP |
Sources: 11-agentic-protocols/README.md33-42 11-agentic-protocols/README.md83-100 12-context-engineering/README.md71-96 13-agent-memory/README.md20-25 15-browser-use/README.md1-6