What is the structured approach for an Agentic Coding?

[udapy]

1. Master Context Engineering to Escape the "Dumb Zone"

AI struggles with complex ("brownfield") codebases because overloading the context window leads to hallucinations and diminishing returns—the "Dumb Zone." The solution is Context Engineering: actively curating the information fed to the model. By prioritizing "better tokens in," you ensure the AI remains objective and effective rather than getting lost in the noise of a large codebase.

2. Use Sub-Agents for "Intentional Compaction"

To keep the main context window lean, employ sub-agents to explore the codebase and return only concise summaries. This strategy, called Intentional Compaction, prevents the parent agent from absorbing unnecessary raw data. It ensures that the context remains highly relevant and compressed, allowing new interaction turns to start efficiently without carrying the baggage of the entire file history.

3. Enforce the RPI Workflow (Research, Plan, Implement)

Do not outsource the actual thinking to the AI.

Instead, use the RPI Workflow to amplify "human intent" :

• Research: The AI generates a concise research document on the system.
• Plan: The AI creates a specific plan with file names and logic (a "compression of intent").
• Implement: The AI executes the code. Crucially, a human must review the Research and Plan phases to ensure alignment before implementation begins.

[udapy]

The Example Scenario:

You have a legacy Python/Flask monolith. You need to migrate the user session management from a local in-memory dictionary (fragile, doesn't scale) to a Redis-backed solution

Phase 1: Research (Discovery & Compression)

The Logic:
You cannot safely modify a system until you have compressed its current state into accurate context.
The goal here is to map dependencies without generating a single line of code.

User Prompt:

"Act as a Lead Architect. Scan the auth_controller.py and session_utils.py files. Do not write code. Instead, generate a Research Summary that lists:
- Where the session dictionary is currently defined.
- Every function reading/writing to that dictionary.
- Potential side effects if this dictionary is replaced by a network call (Redis)."

AI Agent Output:

### Research Summary: Session Management Analysis

#### 1. Current State Definition

Location: session_utils.py (Line 14)

Definition: GLOBAL_SESSION_STORE = {} (In-memory Python Dictionary)

#### 2. Dependency Map The following functions interact directly with GLOBAL_SESSION_STORE:

- login(user_id) in auth_controller.py: Writes new session keys.
- logout(token) in auth_controller.py: Deletes keys.
- validate_token(token) in session_utils.py: Reads keys to verify access.

#### 3. Migration Risks & Side Effects

- Latency: validate_token is currently an O(1) memory lookup. Moving to Redis introduces network latency. High-traffic endpoints calling this validator may see increased response times.

- Concurrency: The current dict is not thread-safe across multiple workers (Gunicorn). Redis will solve this, but we must handle race conditions if tokens are refreshed/revoked simultaneously.

- Error Handling: The current code assumes the "store" is always available. We must add try/except blocks for redis.ConnectionError to prevent the app from crashing if the Redis instance is unreachable.

Phase 2: Plan (Alignment & Intent)

The Logic:
Hallucinations occur when intent is vague. "Planning" converts abstract requirements into a verifiable blueprint that a human can review before code generation begins.

User Prompt:

"Using the Research Summary above, create a Migration Plan.

- List the exact library to import (redis-py).
- Write the pseudo-code signature for the new RedisSessionAdapter.
- Outline the error handling strategy for Redis connection failures (fallback vs. fail-hard)."

AI Agent Output:

### Migration Plan: In-Memory to Redis

#### 1. Dependencies

Library: redis (via pip install redis)

Config: Need REDIS_HOST and REDIS_PORT added to .env.

#### 2. Component Design: RedisSessionAdapter We will encapsulate Redis logic to match the existing interface.

```python
class RedisSessionAdapter:
    def __init__(self):
        # Initialize Redis connection pool
def get(self, token: str) -> dict:
    # Retrieve session data
    # JSON deserialize the result
def set(self, token: str, data: dict, ttl=3600):
    # JSON serialize data
    # Set key with expiration (TTL)

#### 3. Error Handling Strategy

Strategy: Fail-Safe (Soft).

Logic: If Redis is unreachable during validate_token, we should not crash the request.

Implementation: Catch redis.ConnectionError. Log the critical error explicitly. Return None (treating the user as logged out) rather than throwing a 500 Server Error.

#### 4. Verification

Create tests/test_redis_adapter.py mocking the Redis socket to ensure the app handles a "Connection Refused" scenario gracefully.

*** "Human Action" : ***
The Engineer reads this.
They catch a mistake: "Wait, if Redis is down, we can't just log them out silently.
Let's return a specific 503 Service Unavailable so the frontend knows to retry."
The human corrects the plan now, preventing wasted coding cycles.

Phase 3: Implement (Execution)

The Logic:
Code generation becomes a deterministic translation of the detailed plan.
We keep the context window small to avoid "slop."

User Prompt:

You are a Senior Python Developer. Implement Step 2 and 3 of the approved Plan above.

Context: Use the variable names from the Research phase (GLOBAL_SESSION_STORE is being replaced).

Constraint: Implement the 'Fail-Safe' logic but return a 503 error as per the revised plan.

Output: Only the code for session_utils.py."

AI Agent Output:

import os
import json
import redis
from flask import abort
Initialize Redis Connection
Using environment variables for configuration flexibility
r = redis.Redis( host=os.getenv('REDIS_HOST', 'localhost'), port=int(os.getenv('REDIS_PORT', 6379)), db=0, decode_responses=True # decoding responses to string )

class RedisSessionAdapter: """ Adapter to replace the previous GLOBAL_SESSION_STORE dict. Handles JSON serialization and connection errors. """

@staticmethod
def validate_token(token):
    try:
        session_data = r.get(token)
        if not session_data:
            return None
        return json.loads(session_data)
        
    except redis.ConnectionError as e:
        # REVISED PLAN: Log error and return 503 Service Unavailable
        print(f"CRITICAL: Redis connection failed: {e}")
        abort(503, description="Session storage unavailable")
... rest of the utility functions

[udapy]

If you need template you can use to force your LLM to always adhere to this RPI structure?

• Agentic Coding RPI (Research-Plan-Implement) Workflow System Prompt

Note: Keep system prompts simple

[udapy]

Keep these one-liners handy to paste into your chat whenever you tackle a complex task.

Phase	One-Sentence Prompt Trigger	Why it works
Research	"Scan [files]; map dependencies, current logic, and risks; DO NOT generate code yet."	Forces the AI to read before it writes, preventing hallucinations.
Plan	"Based on the research, write a step-by-step migration plan with pseudo-code and error handling; STOP for my review."	"Compression of intent." It lets you catch logic errors before they become code.
Implement	"Execute Step [X] of the approved plan; output ONLY the code for [filename] with no conversational filler."	Constraints reduce noise ("slop") and focus the model on syntax perfection.

[udapy]

🎯 6 years: Typing code by hand will go the way of punch cards

• The more time I spend on AI-assisted programming the less afraid I am for my job, because it turns out building software—especially at the rate it’s now possible to build— still requires enormous skill, experience and depth of understanding.

• The skills are changing though! Being able to read a detailed specification and transform it into lines of code* is the thing that’s being automated away. What’s left is everything else, and the more time I spend working with coding agents the larger that “everything else” becomes.