Building with AI Agents - The Future of Software Development

AI coding assistants are evolving from helpful autocomplete to autonomous agents that can complete entire tasks. Here's what you need to know about this shift and how to leverage it.

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Table of Contents

1. What Are AI Agents?
2. Current AI Agents
3. How to Work with Agents
4. Real-World Applications
5. Limitations and Risks
6. The Future

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What Are AI Agents?

From Assistants to Agents

Traditional AI assistants:

• You ask a question
• AI gives an answer
• You implement it manually
• Repeat for each step

AI agents:

• You describe the goal
• AI plans the approach
• AI executes multiple steps
• AI iterates until done
• You review the result

The Key Difference: Autonomy

Agents can:

• Break down complex tasks
• Execute code and commands
• Observe results
• Adapt their approach
• Continue until complete

Agentic Loop

1. Receive goal
2. Plan approach
3. Take action (write code, run command)
4. Observe result
5. If not done → return to step 2
6. If done → deliver result

This loop enables much more complex task completion.

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Current AI Agents

Devin (Cognition Labs)

What it is: The first "AI software engineer"

Capabilities:

• Completes multi-step coding tasks
• Uses terminal, browser, editor
• Learns codebases
• Writes and runs tests
• Deploys code

Status: Limited access, waitlist

Best for: Complex, well-defined tasks

Limitations:

• Still makes mistakes
• Needs clear requirements
• Can go in wrong directions
• Expensive

Cascade (Windsurf)

What it is: Built-in agent in Windsurf IDE

Capabilities:

• Multi-file editing
• Command execution
• Test running
• Iterative problem-solving

Status: Available now in Windsurf

Best for: Debugging, refactoring, autonomous fixes

Limitations:

• Can over-reach
• Sometimes tries too much
• Needs monitoring

Claude with Computer Use

What it is: Claude controlling a computer

Capabilities:

• Click buttons
• Type text
• Navigate UIs
• Complete multi-step tasks

Status: Beta, available via API

Best for: Automation, testing, repetitive tasks

Limitations:

• Slower than direct coding
• Can get stuck on complex UIs
• Still experimental

Cursor Composer

What it is: Multi-file editing in Cursor

Capabilities:

• Creates/modifies multiple files
• Understands project context
• Follows patterns
• Generates complete features

Status: Available in Cursor Pro

Best for: Feature implementation

Limitations:

• Not fully autonomous
• Requires guidance
• You verify each step

Open-Source Agents

OpenDevin:

• Open-source Devin alternative
• Community-driven
• Improving rapidly
• Free to use

Aider:

• Terminal-based agent
• Git-integrated
• Works with any model
• Powerful for experienced devs

AutoGPT / AgentGPT:

• General-purpose agents
• Can be configured for coding
• Variable quality

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How to Work with Agents

Principle 1: Clear Goals

Agents need clear objectives:

Too vague:

"Make the app better"

Clear:

"Add user authentication using NextAuth with GitHub 
and Google providers. Create login/logout pages and 
protect the /dashboard route."

Principle 2: Verifiable Success

Define what "done" looks like:

Success criteria:
- User can sign in with GitHub
- User can sign in with Google
- Unauthorized users are redirected from /dashboard
- User session persists across page reloads
- Logout clears the session

Principle 3: Bounded Scope

Limit what the agent can touch:

Constraints:
- Only modify files in /auth and /app/(protected)
- Don't change existing API routes
- Use existing database schema

Principle 4: Checkpoint Reviews

For complex tasks, request checkpoints:

Break this into phases and pause after each:
1. Set up NextAuth configuration
2. Create provider configurations
3. Add login/logout pages
4. Implement route protection

Stop after each phase for my review.

Principle 5: Rollback Ready

Always work in version control:

Before starting:
- Commit current state
- Create a branch

If agent goes wrong:
- Easy to revert
- Can compare changes

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Real-World Applications

Application 1: Bug Fixing

Scenario: CI is failing with cryptic errors

Agent workflow:

1. Read error logs
2. Identify failing tests
3. Trace to source code
4. Understand the bug
5. Implement fix
6. Run tests
7. Iterate until passing

Human role: Describe issue, review fix

Application 2: Feature Implementation

Scenario: Need a complete new feature

Agent workflow:

1. Understand requirements
2. Plan file structure
3. Create base components
4. Add logic/functionality
5. Style appropriately
6. Add tests
7. Connect to existing code

Human role: Define requirements, review implementation

Application 3: Codebase Migration

Scenario: Upgrade React Router v5 to v6

Agent workflow:

1. Scan for v5 patterns
2. Understand each usage
3. Convert to v6 syntax
4. Handle edge cases
5. Run tests
6. Fix any breaks

Human role: Verify migrations, handle complex cases

Application 4: Documentation Generation

Scenario: Code needs documentation

Agent workflow:

1. Analyze codebase structure
2. Read existing code
3. Generate README
4. Create API documentation
5. Add inline comments
6. Create usage examples

Human role: Review accuracy, add context

Application 5: Test Coverage

Scenario: Low test coverage

Agent workflow:

1. Identify untested code
2. Understand functionality
3. Write test cases
4. Cover edge cases
5. Run and verify tests
6. Iterate until coverage target

Human role: Review test quality, add edge cases

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Limitations and Risks

Limitation 1: Hallucination

Agents can:

• Invent APIs that don't exist
• Use wrong library versions
• Create fake solutions

Mitigation: Always verify, run tests, review changes

Limitation 2: Context Loss

In long tasks, agents may:

• Forget earlier decisions
• Contradict themselves
• Lose track of the goal

Mitigation: Use checkpoints, break into smaller tasks

Limitation 3: Over-Engineering

Agents sometimes:

• Add unnecessary complexity
• Create over-abstracted solutions
• Build more than asked

Mitigation: Clear constraints, explicit simplicity requests

Limitation 4: Security Blind Spots

Agents may:

• Introduce vulnerabilities
• Miss security considerations
• Use insecure patterns

Mitigation: Security review, use security-focused prompts

Limitation 5: Cost

Agentic workflows:

• Use more tokens
• Take longer
• Cost more than simple queries

Mitigation: Reserve for appropriate tasks, monitor usage

Risk: Over-Reliance

Danger of:

• Not understanding your codebase
• Unable to fix issues without AI
• Losing core skills

Mitigation: Understand the code, learn from agents, stay sharp

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The Future

Near-Term (2025)

• More polished agents (Devin-like tools become common)
• Better IDE integration
• Reduced hallucination
• Faster execution
• More affordable

Medium-Term (2026-2027)

• Agents that truly understand large codebases
• Continuous integration with agents
• AI code review at PR level
• Agents as team members

Long-Term (2028+)

• Natural language as primary interface
• Agents maintaining entire systems
• Human role shifts to architecture/strategy
• "Full-stack" means something different

What Won't Change

• Need for clear thinking
• Importance of understanding
• Human judgment on trade-offs
• Accountability and ownership

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Getting Started with Agents

Step 1: Try Cascade (Easiest)

1. Install Windsurf
2. Open a project
3. Use Cascade for a bug fix
4. Observe how it works

Step 2: Use Cursor Composer

1. Install Cursor
2. Learn multi-file editing
3. Try implementing a feature
4. Guide the process

Step 3: Experiment with OpenDevin

1. Clone the repo
2. Set up locally
3. Try simple tasks
4. Learn the patterns

Step 4: Build Your Own (Advanced)

1. Use Claude/GPT-4 API
2. Add tool use capabilities
3. Create planning logic
4. Build your custom agent

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Best Practices Summary

1. Clear goals - Know what done looks like
2. Bounded scope - Limit what agent can change
3. Checkpoints - Review at intervals
4. Version control - Always be able to rollback
5. Verify everything - Agents make mistakes
6. Learn the code - Don't just accept blindly
7. Start small - Build trust with simple tasks

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Conclusion

AI agents represent a fundamental shift in how we build software. They're not replacing developers—they're amplifying them.

The winners will be developers who:

• Learn to direct agents effectively
• Maintain deep understanding
• Use agents for leverage, not crutches
• Stay curious about capabilities

The future is agentic. Start learning now.

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