autohandai Agentic development principles (Agentic Development Principles)
"AI is the copilot; you are the pilot" AI agents amplify the developer's thinking and take over repetitive work, but final decision-making authority and responsibility always remain with the developer.
When to use this skill
- When starting a collaboration session with an AI agent
- When deciding an approach before starting a complex task
- When establishing a context management strategy
- When reviewing workflows to improve productivity
- When onboarding teammates on how to collaborate with AI
- When applying baseline principles while adopting a new AI tool
Principle 1: Divide and conquer (Divide and Conquer)
Core concept
AI performs much better with small, clear instructions than with large, ambiguous tasks.
How to apply
| Wrong example | Right example |
|---|---|
| "Build me a login page" | 1. "Create the login form UI component" |
| 2. "Implement the login API endpoint" | |
| 3. "Wire up the authentication logic" | |
| 4. "Write test code" | |
| "Optimize the app" | 1. "Analyze performance bottlenecks" |
| 2. "Optimize database queries" | |
| 3. "Reduce frontend bundle size" |
Practical pattern: staged implementation
Step 1: Design and validate the model/schema
Step 2: Implement core logic (minimum viable functionality)
Step 3: Connect APIs/interfaces
Step 4: Write and run tests
Step 5: Integrate and refactor
Validation points
- Can each step be validated independently?
- If it fails, can you fix only that step?
- Is the scope clear enough for the AI to understand?
Principle 2: Context is like milk (Context is like Milk)
Core concept
Context (the AI's working memory) should always be kept fresh and compact.
- Old and irrelevant information reduces AI performance
- Context drift: mixing multiple topics can reduce performance by up to 39% (research)
Context management strategies
Strategy 1: Single-purpose conversation
Session 1: Work on the authentication system
Session 2: Work on UI components
Session 3: Write test code
Session 4: DevOps/deployment work
- Do not mix multiple topics in a single conversation
- Start a new session for a new topic
Strategy 2: HANDOFF.md technique
When the conversation gets long, summarize only the essentials and hand them to a new session:
# HANDOFF.md
## Completed work
- ✅ Implemented user authentication API
- ✅ Implemented JWT token issuance logic
## Current status
- Working on token refresh logic
## Next tasks
- Implement refresh tokens
- Add logout endpoint
## Tried but failed
- Failed to integrate Redis session store (network issue)
## Cautions
- Watch for conflicts with existing session management code
Strategy 3: Monitor context state
- When the conversation gets long, ask the AI to summarize the current state
- If needed, reset the conversation and restart using HANDOFF.md
Strategy 4: Optimization metrics
| Metric | Recommended value | Action |
|---|---|---|
| Conversation length | Keep to a reasonable level | Create HANDOFF.md if it gets long |
| Topic count | 1 (single purpose) | Use a new session for new topics |
| Active files | Only what's needed | Remove unnecessary context |
Principle 3: Choose the right abstraction level
Core concept
Choose an appropriate abstraction level depending on the situation.
| Mode | Description | When to use |
|---|---|---|
| Vibe Coding | High level (see only overall structure) | Rapid prototyping, idea validation, one-off projects |
| Deep Dive | Low level (go line-by-line) | Bug fixes, security review, performance optimization, production code |
In practice
When adding a new feature:
1. High abstraction: "Create a user profile page" → understand overall structure
2. Medium abstraction: "Show the validation logic for the profile edit form" → review a specific feature
3. Low abstraction: "Explain why this regex fails email validation" → detailed debugging
Abstraction level selection guide
- Prototype/PoC: Vibe Coding 80%, Deep Dive 20%
- Production code: Vibe Coding 30%, Deep Dive 70%
- Bug fixes: Deep Dive 100%
Principle 4: Automation of automation (Automation of Automation)
Core concept
If you've repeated the same task 3+ times → find a way to automate it
And the automation process itself → automate that too
Automation level evolution
| Level | Approach | Example |
|---|---|---|
| 1 | Manual copy/paste | AI output → copy into terminal |
| 2 | Terminal integration | Use AI tools directly |
| 3 | Voice input | Voice transcription system |
| 4 | Automate repeated instructions | Use project config files |
| 5 | Workflow automation | Custom commands/scripts |
| 6 | Automate decisions | Use Skills |
| 7 | Enforce rules automatically | Use hooks/guardrails |
Checklist: identify automation targets
- Do you run the same command 3+ times?
- Do you repeat the same explanations?
- Do you often write the same code patterns?
- Do you repeat the same validation procedures?
Automation priority
- High: tasks repeated daily
- Medium: tasks repeated weekly (or more)
- Low: tasks repeated about once a month
Principle 5: Balance caution and speed (Plan vs Execute)
Plan mode (Plan Mode)
Analyze without executing; execute only after review/approval
When to use:
- A complex task you're doing for the first time
- A large refactor spanning multiple files
- Architecture changes
- Database migrations
- Hard-to-roll-back work
Execute mode (Execute Mode)
AI directly edits code and runs commands
When to use:
- Simple, clear tasks
- Work with well-validated patterns
- Sandbox/container environments
- Easy-to-revert work
Recommended ratio
- Plan mode: 70-90% (use as the default)
- Execute mode: 10-30% (only in safe environments)
Safety principles
- ⚠️ Auto-running dangerous commands only in isolated environments
- Always back up before changing important data
- Always use plan mode for irreversible work
Principle 6: Verify and reflect (Verify and Reflect)
How to verify output
Write test code
"Write tests for this function. Include edge cases too."Visual review
- Review changed files via diff
- Revert unintended changes
Draft PR / code review
"Create a draft PR for these changes"Ask for self-verification
"Review the code you just generated again. Validate every claim, and summarize the verification results in a table at the end."
Verification checklist
- Does the code work as intended?
- Are edge cases handled?
- Are there any security vulnerabilities?
- Are tests sufficient?
- Are there any performance issues?
Reflection questions
- What did you learn in this session?
- What could you do better next time?
- Were there repetitive tasks you could automate?
Quick Reference
Six principles summary
| Principle | Core | Practice |
|---|---|---|
| 1. Divide and conquer | Small, clear units | Split into independently verifiable steps |
| 2. Context management | Keep it fresh | Single-purpose conversations, HANDOFF.md |
| 3. Abstraction choice | Depth per situation | Adjust Vibe ↔ Deep Dive |
| 4. Automation² | Remove repetition | Automate after 3 repetitions |
| 5. Plan/execute balance | Caution first | Plan 70-90%, execute 10-30% |
| 6. Verification/reflection | Check outputs | Tests, reviews, self-verification |
Mastery rule
"To truly master AI tools, you need to use them enough"
Learning by using is key - theory alone is not enough; you need to experience different situations in real projects.
Golden rule
When instructing an AI:
1. Clearly (Specific)
2. Step-by-step (Step-by-step)
3. Verifiable (Verifiable)
Best Practices
DO (recommended)
- Focus on one clear goal per conversation
- Regularly clean up context
- Plan before complex work
- Always verify outputs
- Automate repetitive work
DON'T (prohibited)
- Handle multiple unrelated tasks in one conversation
- Keep working with a bloated context
- Auto-run dangerous commands carelessly
- Use AI output as-is without verification
- Repeat the same work without automating it