》𝗦𝘁𝗲𝗽 𝟭: Define the Agent’s Role and Goal
✸ What will your agent do?✸ Who is it helping?
✸ What kind of output will it generate?
→ Example: A medical assistant agent that reads X-rays, summarizes findings, and speaks results.
》𝗦𝘁𝗲𝗽 𝟮: Design Structured Input & Output
✸ Use Pydantic AI or JSON Schemas to define what the agent receives and returns.
✸ Avoid messy text — think like an API.
→ Tool: Pydantic AI, LangChain Output Parsers
》𝗦𝘁𝗲𝗽 𝟯: Prompt and Tune the Agent’s Behavior
✸ Start with role-based system prompts
✸ Use Prompt Tuning or Prefix Tuning for consistent persona and task behavior
→ Tools: GPT-4, Claude, Prefix Tuning, Prompt Tuning
》𝗦𝘁𝗲𝗽 𝟰: Add Reasoning and Tool Use
✸ Equip the agent with reasoning frameworks:
☆ ReAct (Reasoning + Action)
☆ Chain-of-Thought
✸ Allow access to tools like web search, code interpreters, or document retrievers.
→ Tools: LangChain, OpenAI Tools, ReAct Framework
》𝗦𝘁𝗲𝗽 𝟱: Structure Multi-Agent Logic (if needed)
✸ Use orchestration frameworks to define agent roles and coordination.
✸ Create Planner, Researcher, Reporter agents — each with its own input/output schema.
→ Tools: CrewAI, LangGraph, OpenAI Swarm
》𝗦𝘁𝗲𝗽 𝟲: Add Memory and Long-Term Context
✸ Does your agent need to remember what happened earlier?
✸ Use conversational memory, summary memory, or vector-based memory.
→ Tools: Zep, LangChain Memory, Chroma
》𝗦𝘁𝗲𝗽 𝟳: Add Voice or Vision Capabilities (Optional)
✸ Text-to-speech: Use Coqui or ElevenLabs
✸ Image understanding: Use GPT-4o or LLaMA 3.2 Vision
→ Let your agent see and speak.
》𝗦𝘁𝗲𝗽 𝟴: Deliver the Output (in Human or Machine Format)
✸ Format outputs into Markdown → PDF or structured JSON
✸ Output must be both readable and parsable
→ Tools: Pydantic AI, Markdown-to-PDF, LangChain Output Parsers
》𝗦𝘁𝗲𝗽 𝟵: Wrap in a UI or API (Optional)
✸ Create a front-end or expose your agent via API
✸ Use Gradio, Streamlit, or FastAPI
→ This is what turns your agent into a product.
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