Building AI Agents: Reasoning, Planning, Tools and Autonomous Workflows
“A chatbot answers questions. An agent solves problems.”
Introduction
So far in this series, we have learned:
- Part 1 — LLM Fundamentals
- Part 2 — Embeddings and Vector Databases
- Part 3 — RAG Architecture
- Part 4 — Building RAG Applications
- Part 5 — Prompt Engineering
- Part 6 — Spring AI Deep Dive
At this stage, we can build:
✅ Chatbots
✅ Knowledge assistants
✅ RAG systems
✅ Document search applications
But modern AI is moving beyond answering questions.
The next evolution is:
AI Agents
Agents can:
- Reason
- Plan
- Decide
- Use tools
- Observe results
- Take additional actions
This is where AI starts behaving less like software and more like a digital employee.
Chatbot vs Agent
Chatbot
User
↓
LLM
↓
Answer
Agent
User
↓
Reason
↓
Plan
↓
Select Tools
↓
Execute
↓
Observe
↓
Respond
Example
User:
Find my open Jira tickets, summarize them, identify blockers, and send me an email.
A chatbot may answer:
You can check Jira.
An agent:
- Connects to Jira.
- Retrieves tickets.
- Summarizes them.
- Identifies blockers.
- Generates an email.
- Sends it.
What Makes an Agent?
Four capabilities:
| Capability | Purpose |
|---|---|
| Reasoning | Understand problem |
| Planning | Decide steps |
| Tools | Execute actions |
| Memory | Maintain state |
Traditional Software
if(orderAmount > 1000) {
approve();
}
Rules are predefined.
Agent Systems
Goal:
Process order.
Agent decides:
- Check inventory.
- Verify customer.
- Calculate risk.
- Approve order.
The workflow is dynamic.
The Agent Loop
Most agents follow:
Think
Act
Observe
Repeat
The ReAct Pattern
One of the most important concepts.
ReAct means:
Reason + Act
Example:
User:
What is the weather in Delhi and should I carry an umbrella?
Agent:
Thought
I need weather information.
Action
Call weather service.
Observation
Rain expected.
Final Answer
Carry an umbrella.
ReAct Architecture
Question
↓
Thought
↓
Action
↓
Observation
↓
Thought
↓
Final Answer
Example: Architecture Agent
User:
Review my microservice architecture.
Agent reasoning:
Thought:
I need architecture documents.
Action:
Search vector database.
Observation:
Found HLD and LLD.
Thought:
Identify risks.
Action:
Analyze architecture.
Final Answer:
Recommendations.
Planning Agents
Complex tasks require plans.
Example:
Build a deployment strategy.
Plan:
- Analyze architecture.
- Evaluate scalability.
- Recommend infrastructure.
- Produce design.
Planning Architecture
Goal
↓
Planner
↓
Tasks
↓
Execution
↓
Result
Example
User:
Migrate my monolith to microservices.
Planner:
- Analyze application.
- Identify domains.
- Design APIs.
- Recommend deployment.
Agent Memory
Agents need memory.
Without memory:
User:
My project uses Kafka.
User:
Suggest messaging improvements.
AI:
What messaging system?
With memory:
User:
My project uses Kafka.
User:
Suggest improvements.
AI:
Since you're using Kafka...
Short-Term Memory
Current conversation.
Long-Term Memory
Persistent knowledge.
Examples:
- User preferences
- Project information
- Previous decisions
Tool Calling
Tools give agents superpowers.
Examples:
| Tool | Purpose |
|---|---|
| Database | Query data |
| REST API | Fetch information |
| Calendar | Schedule |
| Send messages | |
| Vector Store | Search documents |
| Jira | Retrieve tickets |
Example Tool
public class HolidayTool {
public String calculateDate() {
return "2026-07-15";
}
}
The agent decides when to use it.
Tool Selection
User:
Calculate the deadline.
Agent:
Thought:
Need holiday calendar.
Action:
Call Holiday Service.
Observation:
15 July.
Final Answer:
Deadline is 15 July.
Multi-Step Execution
User:
Prepare a weekly status report.
Agent:
- Retrieve Jira tickets.
- Read emails.
- Analyze commits.
- Generate report.
- Send email.
Autonomous Workflows
Traditional:
User
↓
Application
↓
Response
Agent:
Goal
↓
Plan
↓
Multiple Actions
↓
Result
Agent Components
User
↓
Planner
↓
Memory
↓
Tools
↓
LLM
↓
Response
Agent Types
1. Tool Agent
Uses external APIs.
Example:
- Weather
- Databases
2. Planning Agent
Creates tasks.
3. Research Agent
Searches information.
4. Coding Agent
Writes code.
5. Workflow Agent
Executes business processes.
Enterprise Agent Examples
Support Agent
- Search tickets.
- Find solutions.
Architecture Agent
- Analyze HLD.
- Identify risks.
Incident Agent
- Read logs.
- Suggest root causes.
DevOps Agent
- Analyze deployment failures.
HR Agent
- Answer policy questions.
Java Example
@Service
public class ArchitectureAgent {
public String review(String architecture) {
return chatClient.prompt()
.system("""
You are an architect.
Analyze risks.
Recommend improvements.
""")
.user(architecture)
.call()
.content();
}
}
Spring AI and Agents
Spring AI provides:
- ChatClient
- Memory
- Tools
- Advisors
This enables agent development.
Agent State
Agents maintain:
Goal
Current Task
Completed Tasks
Observations
Results
Example
Goal:
Deploy application.
State:
Task 1 Completed.
Task 2 Failed.
Retry required.
Reflection
Advanced agents can review their own work.
Example:
Generated answer.
Review answer.
Improve answer.
Return final version.
This increases quality.
Agent Guardrails
Agents are powerful.
Therefore they require limits.
Examples:
- Maximum steps.
- Approved tools.
- Human approval.
- Cost limits.
Human-in-the-Loop
Example:
Agent:
Ready to deploy.
Human:
Approve.
Agent:
Proceed.
Very important in enterprises.
Agent Architecture
User
↓
Planner
↓
Reasoner
↓
Tool Executor
↓
Memory
↓
Response
AI Agent Example for Your Architecture
Imagine:
Enterprise Architecture Agent
Capabilities:
- Read HLD.
- Read LLD.
- Search Confluence.
- Query Jira.
- Analyze APIs.
- Generate recommendations.
User:
Review our deadline processing system.
Agent:
- Reads architecture.
- Searches requirements.
- Reviews APIs.
- Suggests improvements.
Production Incident Agent
Input:
- Logs
- Metrics
- Traces
Agent:
- Analyze failures.
- Find similar incidents.
- Suggest root cause.
Cost Considerations
Agents can make multiple calls.
Example:
Plan
Search
Analyze
Review
Summarize
5 LLM calls.
Monitoring costs becomes important.
Failure Handling
Tools may fail.
Example:
Weather API unavailable.
Agent:
- Retry.
- Use fallback.
- Ask user.
Observability
Track:
- Steps executed.
- Tools called.
- Latency.
- Cost.
- Errors.
Java Analogy
| Java | Agents |
|---|---|
| Service | Agent |
| Method | Tool |
| Workflow | Plan |
| Session | Memory |
| Business Rules | Reasoning |
| State Machine | Agent State |
Interview Questions
What is an AI agent?
A system that can reason, plan, and act.
What is ReAct?
Reason plus action.
Why are tools important?
They allow interaction with external systems.
What is agent memory?
Persistent context.
Why human approval?
To reduce risks.
Hands-On Exercise
Build:
Jira Status Agent
Features:
- Retrieve tickets.
- Summarize issues.
- Identify blockers.
- Generate email.
Enterprise Project
Production Support Agent
Tools:
- Logs API
- Monitoring API
- Incident database
Capabilities:
- Root cause analysis.
- Similar incidents.
- Recommendations.
Key Takeaways
✔ Agents reason.
✔ Agents plan.
✔ Agents use tools.
✔ Agents maintain memory.
✔ Agents execute workflows.
✔ Agents can work autonomously.
✔ Guardrails are essential.
What’s Next?
Part 8 — Multi-Agent Systems and Agentic AI Architectures
Topics:
- Supervisor agents.
- Team agents.
- Hierarchical agents.
- Agent communication.
- Agent orchestration.
- Enterprise multi-agent systems.
- LangGraph.
- Agent workflows.
Because one intelligent agent is useful.
Multiple collaborating agents may transform enterprise software.
“The most exciting question in AI is no longer ‘What can the model answer?’ but rather ‘What can the agent accomplish?'”
