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Agent workflow (input → thinking → output)

Intermediate⏱ 10 min read📅 Updated: 2026-02-17

⚡ Introduction – Inside the Agent's Mind

Oru AI Agent ku task kodukka, output varum. But naduvula enna nadakkudhu? 🤔

Agent internally oru structured workflow follow pannum:

Input → Parse → Think → Plan → Act → Evaluate → Output

Idha understand pannaa, neenga:

🔧 Better agents build pannalaam
🐛 Debugging easier aagum
🎯 Output quality control pannalaam
⚡ Performance optimize pannalaam

Let's open the black box and see what's inside! 📦

📥 Step 1: Input Processing

Agent-ku varum input-a first process pannum:

Input Types:

Input Type	Example	Processing
Text	User message	NLP parsing
Data	CSV, JSON	Structure extraction
Events	API webhook	Event classification
Multi-modal	Image + text	Multi-model processing
Context	Conversation history	Context window management

Input Processing Pipeline:

Receive – Raw input accept pannum
Validate – Input format verify pannum
Classify – What type of request? Categorize pannum
Enrich – Add context (user history, preferences, time)
Normalize – Standard format-ku convert pannum

Example:

code

Raw Input: "tmrw flight to blr book pannunga cheap one"

After Processing:
- Intent: flight_booking
- Destination: Bangalore (BLR)
- Date: Tomorrow (2026-02-18)
- Preference: Low cost
- Context: User location = Chennai

Good input processing = better reasoning downstream! 🎯

🧠 Step 2: Reasoning / Thinking

Heart of the agent – idhu dhaan magic nadakkum idam! 🪄

Agent's reasoning engine (LLM) indha process follow pannum:

2a. Situation Assessment 📊

Current state enna? What do I know?
What's missing? What do I need?
Any constraints or limitations?

2b. Strategy Formation 🎯

Multiple approaches consider pannum
Pros/cons evaluate pannum
Best strategy select pannum

2c. Task Decomposition 📋

Big task → small sub-tasks
Dependencies identify pannum
Execution order determine pannum

Reasoning Patterns:

Pattern	How It Works	Best For
Chain-of-Thought	Step-by-step thinking	Complex problems
ReAct	Reason → Act → Observe loop	Tool-using tasks
Tree-of-Thought	Explore multiple paths	Creative tasks
Reflection	Self-critique and improve	Quality-critical tasks
Plan-and-Execute	Plan first, then execute	Multi-step workflows

Pro tip: Better reasoning pattern = better agent output! Choose wisely! 🧠

🏗️ Complete Agent Workflow Architecture

🏗️ Architecture Diagram

```
┌─────────────────────────────────────────┐
│  📥 INPUT LAYER                         │
│  User Message │ API Event │ Schedule    │
└────────────────────┬────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────┐
│  🔍 PARSING & ENRICHMENT               │
│  Intent Detection │ Entity Extraction   │
│  Context Loading  │ History Retrieval   │
└────────────────────┬────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────┐
│  🧠 REASONING ENGINE (LLM)             │
│  ┌────────────┐ ┌────────────────────┐ │
│  │ Assess     │ │ Plan               │ │
│  │ Situation  │ │ Sub-tasks          │ │
│  └────────────┘ └────────────────────┘ │
│  ┌────────────┐ ┌────────────────────┐ │
│  │ Select     │ │ Determine          │ │
│  │ Strategy   │ │ Tools Needed       │ │
│  └────────────┘ └────────────────────┘ │
└────────────────────┬────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────┐
│  🔧 TOOL EXECUTION                     │
│  API Call │ DB Query │ Web Search       │
│  Code Run │ File I/O │ External Service │
└────────────────────┬────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────┐
│  🔄 OBSERVATION & EVALUATION            │
│  ┌────────────────┐ ┌────────────────┐ │
│  │ Parse Results  │ │ Check Quality  │ │
│  └────────────────┘ └────────────────┘ │
│  ┌────────────────┐ ┌────────────────┐ │
│  │ Goal Achieved? │ │ Need Retry?    │ │
│  └────────────────┘ └────────────────┘ │
│          │ NO              │ YES       │
│          │    ┌────────────┘           │
│          │    │ Back to Reasoning      │
│          │    └─────────▲──────────────│
└──────────┼──────────────┼─────────────┘
           │ YES          │
           ▼
┌─────────────────────────────────────────┐
│  📤 OUTPUT GENERATION                   │
│  Format │ Validate │ Deliver            │
└─────────────────────────────────────────┘
```

📋 Step 3: Planning

Reasoning aana next, agent detailed plan create pannum:

Planning Components:

Task List 📝

code

Goal: Book cheap flight to BLR tomorrow
├── Task 1: Search available flights
├── Task 2: Compare prices
├── Task 3: Check user preferences (airline, time)
├── Task 4: Select best option
├── Task 5: Complete booking
└── Task 6: Send confirmation

Tool Selection 🔧

Task	Tool Needed
Search flights	Flight Search API
Compare prices	Calculator/Logic
Check preferences	User Profile DB
Select best	Reasoning (LLM)
Book flight	Booking API
Send confirmation	Email/Notification API

Dependency Mapping 🔗

Task 2 depends on Task 1 results
Task 4 depends on Task 2 + Task 3
Task 5 depends on Task 4
Task 6 depends on Task 5

Contingency Plans 🛡️

No flights available? → Try next day
Booking fails? → Retry or alternative airline
API down? → Use backup provider

Good planning = smooth execution! 📋

🔧 Step 4: Tool Execution

Plan ready, now agent tools use panni execute pannum:

Tool Execution Flow:

code

Agent: "I need to search flights"
  → Select tool: flight_search_api
  → Prepare params: {from: "MAA", to: "BLR", date: "2026-02-18"}
  → Execute API call
  → Parse response
  → Extract: 5 flights found, prices ₹2,500-₹8,000

Common Tool Types:

Category	Tools	Example
Search	Web search, DB query	Google API, SQL
Communication	Email, messaging	SendGrid, Slack API
Computation	Calculator, code execution	Python sandbox
Data	File read/write, transform	CSV parser, JSON
External APIs	Third-party services	Payment, booking
Memory	Store/retrieve info	Vector DB, Redis

Tool execution best practices:

✅ Validate params before calling
✅ Handle API errors gracefully
✅ Parse responses carefully
✅ Log every tool call for debugging
✅ Set timeout for each call

🎬 Complete Workflow Trace

✅ Example

User Input: "Send a birthday wish to Priya with a nice message"

Trace:

code

[INPUT] "Send birthday wish to Priya"
[PARSE] Intent: send_message, To: Priya, Type: birthday_wish
[ENRICH] Priya's contact: priya@email.com, Relationship: close friend
[REASON] Need to: 1) Generate message 2) Choose channel 3) Send
[PLAN] Step1: Generate personalized birthday message
       Step2: Check preferred channel (email/WhatsApp)
       Step3: Send via preferred channel
       Step4: Confirm delivery
[ACT-1] Tool: LLM → Generate warm birthday message
[OBSERVE-1] Message: "Happy Birthday Priya! 🎂 Wishing you..."
[ACT-2] Tool: contact_db → Get preferred channel
[OBSERVE-2] Priya prefers WhatsApp
[ACT-3] Tool: whatsapp_api → Send message
[OBSERVE-3] Status: delivered ✅
[EVALUATE] Goal achieved? YES ✅
[OUTPUT] "Birthday wish sent to Priya via WhatsApp! 🎉"

Every step visible and traceable! 🔍

🔄 Step 5: Observation & Evaluation

Action execute aana aprom, agent results evaluate pannum:

Evaluation Criteria:

Check	Question	Action if Failed
Completeness	Task fully done?	Continue execution
Correctness	Result accurate?	Retry with different approach
Quality	Output good enough?	Refine and improve
Goal Match	Aligns with original goal?	Re-plan if deviated
Error Check	Any errors occurred?	Handle or escalate

Self-Evaluation Example:

code

[EVALUATE]
✅ Flight search completed - 5 results
✅ Price comparison done - cheapest ₹2,500
⚠️ User preference not checked - need to verify timing
❌ Booking not yet done - proceed to booking step
→ Decision: Check user preference first, then book

Key insight: Good agents are self-critical – mediocre agents just output and stop! 🪞

📤 Step 6: Output Generation

Finally, agent output format panni deliver pannum:

Output Formatting:

Output Type	Format	Example
Text	Natural language	"Your flight is booked!"
Structured	JSON, table	Booking details JSON
Action	System action	API call executed
Multi-modal	Text + image	Report with charts
Notification	Alert/message	Email/SMS sent

Good Output Characteristics:

✅ Clear – User easily understand pannum
✅ Complete – All requested info included
✅ Actionable – User enna pannanum clear aa irukku
✅ Formatted – Readable, organized
✅ Honest – Limitations mentioned if any

Bad Output Example: ❌

"Done."

Good Output Example: ✅

"✈️ Flight booked! Chennai → Bangalore, Feb 18, IndiGo 6E-201,

Departure 8:30 AM, Arrival 9:45 AM. Cost: ₹2,500.

Confirmation #: IND-789456. E-ticket sent to your email."

🔁 The Iteration Loop

Agent workflows rarely single-pass la finish aagum. Iteration is key!

Common Loop Patterns:

1. Retry Loop 🔄

code

Attempt 1: API call → Timeout
Attempt 2: API call → Success ✅

2. Refinement Loop ✨

code

Draft 1: Generate content → Quality check: 6/10
Draft 2: Improve content → Quality check: 8/10 ✅

3. Exploration Loop 🔍

code

Search 1: Query "cheap flights" → 3 results
Search 2: Query "budget airlines BLR" → 5 more results
Combine: Best of all 8 results ✅

4. Correction Loop 🔧

code

Action: Book flight → Error: seat unavailable
Adjust: Try next cheapest option
Action: Book alternative → Success ✅

Iteration limits set pannunga! Otherwise infinite loops possible! ⚠️

Typical: max_iterations = 10, timeout = 60 seconds

🧪 Try It – Trace Your Own Workflow

📋 Copy-Paste Prompt

```
You are an AI Agent with these tools:
- web_search(query) → search results
- calculate(expression) → number
- send_email(to, subject, body) → confirmation
- get_weather(city, date) → weather data

For the following request, show your COMPLETE workflow:
1. [INPUT] Show how you parse the input
2. [REASON] Show your thinking process
3. [PLAN] List your step-by-step plan
4. [ACT] Execute each step (simulate tool calls)
5. [OBSERVE] Show what you learned from each tool
6. [EVALUATE] Self-check your work
7. [OUTPUT] Deliver final response

Request: "I'm planning a picnic in Chennai this Saturday. 
Help me plan – check weather, suggest good spots, 
and email the plan to my friend at raj@gmail.com"

Show every step with labels!
```

Observe the full workflow trace – super educational! 📚

💡 Workflow Optimization Tips

💡 Tip

Speed up your agent workflows:

1. Parallel Execution ⚡ – Independent tool calls simultaneously run pannunga

2. Caching 💾 – Same query ku same result? Cache it!

3. Early Exit 🚪 – Goal achieved early aa? Stop the loop!

4. Smart Tool Selection 🔧 – Right tool first time la select pannunga

5. Context Pruning ✂️ – Unnecessary info remove pannunga from context

Performance benchmarks:

| Optimization | Speed Improvement |

|-------------|-------------------|

| Parallel tools | 40-60% faster |

| Response caching | 30-50% fewer API calls |

| Early exit | 20-40% less iterations |

| Context pruning | 15-25% faster reasoning |

🐛 Debugging Agent Workflows

Agent expected output tharaala? Debug panna:

1. Trace Logging 📋

Every step log pannunga – input, reasoning, tool calls, output

2. Step-by-Step Inspection 🔍

Which step la wrong output vandhuchu? Isolate and fix

3. Common Issues:

Problem	Likely Cause	Fix
Wrong tool selected	Poor tool descriptions	Better tool docs
Infinite loop	No exit condition	Add max iterations
Bad output format	No format instructions	Add output schema
Missing info	Incomplete parsing	Better input processing
Hallucinated data	No tool verification	Always verify with tools

4. Testing Strategy 🧪

Unit test each workflow step
Integration test full workflow
Edge case testing (empty input, API failures)
Load testing (concurrent requests)

Golden rule: If you can't trace it, you can't debug it! 📋

📝 Summary

Key Takeaways:

✅ Agent workflow: Input → Parse → Reason → Plan → Act → Evaluate → Output

✅ Input processing – validate, classify, enrich, normalize

✅ Reasoning – Chain-of-Thought, ReAct, Tree-of-Thought patterns

✅ Planning – Task decomposition, tool selection, dependency mapping

✅ Tool execution – API calls, DB queries, computations

✅ Evaluation – Self-critique, quality checks, goal verification

✅ Output – Clear, complete, actionable, well-formatted

✅ Iteration – Retry, refine, explore, correct loops

✅ Debug with trace logging and step-by-step inspection

Next article la Memory in Agents paapom – agents epdi remember pannும்! 🧠💾

🏁 🎮 Mini Challenge

Challenge: Trace Complete Agent Workflow

Agent workflow na fully understand panna hands-on trace:

Your Task: "Convert ₹1000 to USD, compare with gold price, send summary email"

Step-by-step Trace (15 mins):

[INPUT] "₹1000 to USD kaen? Gold price compare pannu, summarize pannunga"

[PARSE]

Intent: currency_conversion + comparison
Entities: Amount=1000, From=INR, To=USD, Compare=gold
Context: User wants summary email

[REASON]

Need 3 data points: USD rate, INR amount, gold price
Decision: Call currency API first, then gold API, format comparison

[PLAN]

Fetch INR→USD rate
Calculate: 1000 INR = ? USD
Fetch gold price (per gram in INR)
Create comparison
Generate summary
Send email

[EXECUTE]

[ACT 1] API call → exchange_rate(INR, USD) → 1 INR = 0.012 USD → 1000 INR = ₹12

[ACT 2] API call → gold_price() → ₹7500/gram

[ACT 3] Compare: $12 USD = ~0.0016 grams gold

[ACT 4] Format output

[ACT 5] send_email(user@email.com, summary)

[EVALUATE]

All steps completed? ✅
Quality OK? ✅
Ready to output? ✅

[OUTPUT]

"1000 INR = $12 USD. Current gold price: ₹7500/gram. So $12 = 0.0016g of gold. Summary sent to your email! 📧"

Complete trace pannaa, workflow architecture clear aagum! 🔍

💼 Interview Questions

Q1: Agent workflow-la input processing edhuku critical?

A: Garbage input → garbage reasoning → garbage output. Input processing good aa irundha (intent clear, entities extracted, context enriched), whole workflow smooth aagum. First step proper aa, rest easy!

Q2: Reasoning patterns – which when use pannanum?

Chain-of-Thought: Step-by-step thinking complex problems-ku
ReAct: Tool-using tasks-ku (most common)
Tree-of-Thought: Multiple solutions explore panna-onum (creative)
Reflection: Quality improvement-ku (iterative)

Select pattern = task type match pannunga!

Q3: Agent workflow la iteration loop necessary aa?

A: Very! First attempt wrong result varalaam. Retry, refine, explore loops necessary:

Retry: API fail, retry
Refine: Quality low, improve
Explore: Multiple solutions find

Without loops, agent fragile aagum!

Q4: Agent workflow la biggest bottleneck enna?

Reasoning latency (LLM slow)
Tool execution time (APIs slow)
Context window limits
Communication overhead

Optimize: parallel tools, caching, context pruning, smaller models use!

Q5: Workflow debugging best practice?

Trace logging (every step detail)
Step-by-step inspection (isolated testing)
Unit test steps (individual validation)
Integration test (full flow)
Edge cases (empty input, API errors)

If you can't trace, you can't debug! Logging = critical! 📋

❓ Frequently Asked Questions

❓ Agent workflow la most important step enna?

Reasoning/Thinking step – idhu dhaan agent-oda brain. Wrong reasoning = wrong output. LLM quality and prompt design indha step-a heavily influence pannum.

❓ Agent workflow loop infinite aa run aaguma?

Possible! Adhaan max iterations limit set pannanum. Usually 5-15 iterations max. Budget limits and timeout mechanisms-um implement pannanum.

❓ Workflow la error handling epdi panradhu?

Try-catch at each step, fallback strategies, retry mechanisms, and graceful degradation. Critical errors ku human escalation path irukanum.

❓ Agent thinking process influence panna mudiyuma?

Yes! System prompt, few-shot examples, and structured output formats through reasoning guide pannalaam. Chain-of-thought prompting thinking quality improve pannum.

🧠Knowledge Check

Quiz 1 of 1

Test your workflow knowledge:

0 of 1 answered

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Agent workflow (input → thinking → output)

⚡ Introduction – Inside the Agent's Mind

📥 Step 1: Input Processing

🧠 Step 2: Reasoning / Thinking

🏗️ Complete Agent Workflow Architecture

📋 Step 3: Planning

🔧 Step 4: Tool Execution

🎬 Complete Workflow Trace

🔄 Step 5: Observation & Evaluation

📤 Step 6: Output Generation

🔁 The Iteration Loop

🧪 Try It – Trace Your Own Workflow

💡 Workflow Optimization Tips

🐛 Debugging Agent Workflows

📝 Summary

🏁 🎮 Mini Challenge

💼 Interview Questions

❓ Frequently Asked Questions