AI Agents Crash Course – Part 2: How AI Agents Think

Why Reasoning Matters

At the heart of every effective AI agent lies its ability to reason.

AI agents aren’t just responding to prompts — they’re planning, reflecting, and collaborating to solve real-world problems.

This post explores the most important reasoning strategies used by high-performing agents today, followed by a real-world case study to show them in action.

🔍 1. Plan-and-Execute

How it works: The agent develops a detailed plan, breaks it into executable steps, and runs those steps in sequence. It can re-evaluate and adapt based on context or failures.

Example: LangGraph uses this approach to power agents handling complex task decomposition in logistics or supply chain workflows.

🤔 2. Self-Discovery

How it works: Agents test different methods to solve a task and learn from outcomes — using self-critique loops to refine their output over time.

Example: CrewAI agents use self-discovery to iteratively draft and improve content, increasing clarity, tone, and value with each loop.

🧭 3. Hierarchical Supervision

How it works: A supervisory agent coordinates lower-tier agents, providing structure, accountability, and logic flow.

Example: IBM’s healthcare diagnostics model includes specialist agents (e.g., cardiology, oncology) reporting to a central supervisory agent that manages coordination and final decisions.

🤝 4. Multi-Agent Collaboration

How it works: Specialized agents work together in parallel, sharing context and results across a common objective.

Example: In a customer support scenario, one agent researches the issue, another drafts the reply, and a third reviews tone and brand alignment.

🧪 Case Study: Multi-Agent Customer Support System

Objective: Build an AI-powered support system for complex customer inquiries.

👇 Agent Roles & Tools:

• Researcher Agent

  • Tool: ChromaDB for vector-based search

  • Task: Retrieve accurate, relevant answers

• Resolver Agent

  • Tool: GPT-4 Turbo

  • Task: Draft a complete, human-like response

• Reviewer Agent

  • Tool: Sentiment analysis API

  • Task: Check for tone, completeness, brand alignment

🧭 Workflow:

1. Customer sends a query via chat or email.

2. Researcher Agent pulls context from internal knowledge base.

3. Resolver Agent drafts a response using retrieved content.

4. Reviewer Agent validates clarity and tone.

5. If approved, it’s sent to the customer. If not, it’s sent back for refinement.

Impact: Faster resolutions, more accurate responses, higher customer satisfaction.

✅ Action Step

Pick one reasoning strategy from above and map it to a task or workflow.

🔜 What’s Next?

In the next part of the series, we’ll explore how to choose the right frameworks and tools to start building your own AI agents — plus, you’ll get a hands-on breakdown of key agent stacks (LangChain, CrewAI, and more).

Stay tuned,

Abhishek Sisodia

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