Tuesday, January 06, 2026

How Junior Devs Can Learn Agent Orchestration (Practically)

Orchestrate and use agents

The Core Principle

Don’t teach “agents.” Teach pipelines with autonomy.

A junior dev should ship one narrow workflow that:

Calls tools
Handles failure
Controls cost
Produces logs
Is testable
Has numbers to show

No multi-agent chaos. No “AI swarm.”

The One Workflow to Ship (Example)

🎯 Project: “Support Ticket Triage Agent”

Input: Raw support ticket text
Output:

Category (bug / billing / feature)
Priority (low / med / high)
Draft response
Confidence score

This is:

Useful
Boring enough to finish
Rich enough to teach orchestration

Step-by-Step: The Learning Path

1. Define the Agent as a State Machine (Not Magic)

Teach juniors to think in states, not prompts.


START
 ↓
Classify Ticket
 ↓
If confidence < threshold → Retry w/ different prompt
 ↓
Draft Response
 ↓
Validate Output
 ↓
END

This alone removes 80% of agent confusion.

2. Tool Calling (Minimal but Real)

Tools to include

classify_ticket(text)
draft_response(category, priority, text)
log_event(event_type, metadata)

Example (Python-ish pseudocode):


response = client.responses.create(
    model="gpt-4.1-mini",
    input=ticket_text,
    tools=[classify_tool, response_tool]
)

📌 Teach:

Tools are typed functions
The model chooses when to call them
You own execution + validation

3. Retries (The First “Agentic” Behavior)

Retry logic should be deterministic, not vibes.

Example:


MAX_RETRIES = 2

for attempt in range(MAX_RETRIES):
    result = classify(ticket)
    if result.confidence > 0.7:
        break
    log("retry", {"attempt": attempt})

Teach juniors:

Retry on low confidence
Change instructions, not randomness
Log every retry

4. Logging (Non-Negotiable)

Every run logs:

Field	Example
request_id	`abc123`
model	`gpt-4.1-mini`
tokens_in	412
tokens_out	96
cost	`$0.0021`
retries	`1`
latency	`842ms`

Use:

JSON logs
One log per step
No screenshots yet — raw data first

This teaches observability, not prompt tweaking.

5. Cost Controls (Make It Explicit)

Hard limits:


if total_cost_today > 5.00:
    raise BudgetExceeded()

Soft limits:

Smaller model for classification
Bigger model only for response drafting
Cap retries

📌 Juniors should print cost per run in the console.

That feedback loop matters.

6. Basic Test Harness (This Is Where Most Skip)

Teach evaluation before scaling.

Golden test cases (10–20)


{
  "input": "I was charged twice",
  "expected_category": "billing",
  "expected_priority": "high"
}

Test:

Accuracy
Confidence calibration
Cost per test

Run nightly or manually.

7. Write It Up (This Is the Career Accelerator)

The write-up should include:

📸 Screenshots

Logs
Retry happening
Tool call JSON
Cost summary

📊 Numbers

Accuracy before retry vs after
Avg cost per ticket
Avg latency
Failure rate

🧠 What They Learned

Where the agent fails
What retries help vs hurt
How cost scales

This is portfolio gold.

What This Teaches (Quietly)

Without buzzwords, juniors learn:

Agent orchestration
Deterministic control flow
LLM failure modes
Cost-aware design
Evaluation discipline
Production thinking

They stop being “prompt engineers” and start being systems engineers.

Adhyayan