A practical guide for working effectively with AI coding agents, especially Claude Code as of begin 2026. Things keep changing a fast.
Core Principles
1. Model Intelligence = Less Prompting Required
Smart models like Claude Opus 4.5 understand context and intent with minimal instruction. Be clear and concise rather than over-explaining. Trust the model's reasoning capabilities.
Do this: "Add authentication to the API"
Not this: "I need you to add authentication. First, you should create a middleware function. Then you need to check for tokens. Make sure to validate them properly..."
2. Prevent Context Degradation
Long conversations accumulate noise, outdated information, and conflicting instructions. This "context rot" reduces effectiveness dramatically.
Solutions:
- Start fresh conversations for new tasks
- Summarize and restart when threads get long (>50 exchanges)
- Use separate sessions for unrelated problems
- Archive completed work and begin clean for next feature
Warning signs of context rot:
- Agent repeating previously solved problems
- Inconsistent responses to similar questions
- Ignoring earlier agreements or decisions
- Slower response times or confused outputs
3. Single Task Focus
Agents work best with clear, isolated objectives. Mixing multiple unrelated tasks creates confusion and reduces quality.
Do this: One agent session per feature/bug
Avoid: "Fix the auth bug, add logging, refactor the database layer, and update the docs"
For multiple related tasks: Complete them sequentially in the same session, with clear transitions between each.
Workflow Pattern: Brainstorm → Plan → Execute
For features and complex issues, use this three-phase approach:
Phase 1: Brainstorm
- Explore the problem space
- Generate alternative approaches
- Identify edge cases and dependencies
- Surface hidden complexity early
Example: "Let's brainstorm approaches for rate limiting our API. Consider both user-based and IP-based limits, distributed systems, and cost."
Phase 2: Plan
- Convert insights into concrete tasks
- Break down into manageable units
- Establish clear success criteria
- Create logical execution order
Example: "Based on our brainstorm, create a plan with specific tasks for implementing Redis-based rate limiting."
Phase 3: Execute
- Critical: Start a NEW session for each task with clean context
- Reference the plan but execute one task at a time
- Each task gets full agent attention without baggage from brainstorming
Why separate execution? The brainstorming phase generates many ideas, alternatives, and dead ends. Carrying this into execution clutters the context. Clean slate = better focus.
Agent-Assisted Debugging
Enable powerful automated debugging by giving agents access to system state:
Provide Diagnostic Tools
- MCP servers for log access
- Commands to view service status
- Scripts to dump relevant state
- Direct access to error tracking systems
Effective Debugging Sessions
# Good: Agent can self-serve information
Agent has access to: logs via MCP, kubectl commands, error tracking API
# Less effective: Manual information relay
You: "What does the error say?"
Agent: "I need to see the logs"
You: [copies logs]
Agent: "Can you check the database?"
You: [runs query, pastes results]
Example MCP Tools for Debugging
-
read_logs- Tail application logs -
get_metrics- Fetch system metrics -
query_db- Run diagnostic queries -
list_processes- Check running services -
get_config- View current configuration
Result: Agents can iterate through debugging hypotheses autonomously, dramatically faster than back-and-forth exchanges.
Repository Structure Best Practices
Include an AI-Friendly README
Create .claude/README.md or docs/ARCHITECTURE.md with:
- Project structure overview
- Key design decisions and why
- Common commands and workflows
- Where to find configuration
- Testing approach
Provide Tool Access
Document in your repo root:
# .claude/tools.md
## Available Commands
- `npm run dev` - Start development server
- `npm test` - Run test suite
- `./scripts/db-reset.sh` - Reset local database
## MCP Servers
- Logs: Access via filesystem MCP to `./logs/`
- Database: PostgreSQL MCP configured for local DB
Context Documents
Small, focused files the agent can reference:
-
CONVENTIONS.md- Code style, naming, patterns -
TESTING.md- How to write and run tests -
DEPLOYMENT.md- Release process and environments
Quick Reference
| Situation | Solution |
|---|---|
| Starting a new feature | Fresh conversation, single focus |
| Thread feels confused | Summarize progress, start new session |
| Multiple related tasks | Sequential execution, one at a time |
| Complex feature | Brainstorm → Plan → Execute (new sessions) |
| Debugging is slow | Add MCP tools for log/state access |
| Agent seems "forgetful" | Context rot - restart with summary |
| Need to explain project | Add .claude/README.md with architecture |
Anti-Patterns to Avoid
❌ Marathon sessions - 100+ message threads accumulate noise
❌ Task mixing - "While you're at it, also..." leads to half-finished work
❌ Over-prompting - Smart models don't need hand-holding
❌ Manual debugging - Give agents tools instead of playing telephone
❌ Unclear success criteria - "Make it better" vs. "Reduce load time to <200ms"
❌ Ignoring context rot - Pushing through confusion instead of restarting
Remember
The agent is a specialist, not a generalist. Give it focused problems, clean context, and the tools it needs. You'll get significantly better results than treating it as a general-purpose chatbot.
Context is currency. Spend it wisely. When in doubt, start fresh.
Tools amplify capability. An agent with log access debugs 10x faster than one asking you to copy-paste error messages.
Keep this guide visible in your development workflow. Share with your team. Iterate based on what works for your specific stack and problems.
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