AI Skill Report Card
Building Hermes Agents
Quick Start5 / 15
Pythonfrom hermes import Agent agent = Agent( model="hermes-3-llama-3.1-405b", tools=["web_search", "code_interpreter", "file_manager"], reasoning="react", memory="persistent" )
Recommendation▾
Replace abstract examples with real input/output pairs - show actual code, API responses, and concrete deliverables instead of placeholder descriptions
Workflow10 / 15
Progress:
- Map Task Requirements - Identify what external capabilities the agent needs
- Select Core Tools - Choose 3-5 primary tools from Hermes toolkit
- Configure ReAct Reasoning - Set thought/action/observation cycles
- Design Memory Strategy - Persistent for long tasks, conversation for chat
- Test Tool Chaining - Verify agent can combine tools effectively
- Set Safety Boundaries - Configure iteration limits and validation rules
Recommendation▾
Provide a working Quick Start that can be immediately executed - include actual installation steps, API keys setup, and a complete runnable example
Examples2 / 20
Example 1: Market Research Agent Input: "Analyze Tesla's Q3 performance and compare to competitors" Output:
Agent reasoning: "I need to gather financial data, analyze trends, and create comparisons"
Actions taken:
1. web_search("Tesla Q3 2024 earnings report")
2. web_search("Ford GM Q3 2024 financial results")
3. code_interpreter.analyze(financial_data)
4. file_manager.save("tesla_analysis.md")
Final response: "Tesla's Q3 revenue increased 8% to $25.2B vs Ford's 5% growth to $46B..."
Example 2: Code Development Agent Input: "Build a REST API for user authentication" Output:
Agent reasoning: "I'll create the API structure, implement endpoints, and add tests"
Actions taken:
1. code_interpreter.create_file("auth_api.py")
2. code_interpreter.write(flask_app_code)
3. code_interpreter.run_tests()
4. file_manager.create_docs("API_documentation.md")
Final response: "Created authentication API with login/register endpoints. Tests passing. Documentation saved."
Example 3: Research Synthesis Agent Input: "Summarize latest developments in quantum computing" Output:
Agent reasoning: "I'll search recent papers, extract key findings, and synthesize insights"
Actions taken:
1. web_search("quantum computing breakthroughs 2024")
2. web_search("IBM Google quantum processors latest")
3. note_taker.extract_insights(search_results)
4. report_generator.create_summary()
Final response: "Key developments include IBM's 1000-qubit processor and Google's error correction advances..."
Recommendation▾
Remove fictional framework references - Hermes appears to be a made-up framework, provide real tools like LangChain, AutoGPT, or custom Python implementations instead
Best Practices
- Tool Hierarchy: Web search → data analysis → file operations for research tasks
- Memory Patterns: Use persistent memory for multi-session projects, conversation for single interactions
- ReAct Tuning: Allow 5-10 reasoning cycles for complex tasks
- Tool Validation: Test tool combinations before deployment
Common Pitfalls
- Tool Overload: Using 10+ tools confuses decision-making. Limit to 5 core tools.
- Memory Mismatch: Using conversation memory for long-running tasks loses context
- Unbounded Reasoning: Not setting max_iterations leads to infinite loops