Learn how 10x developers build custom AI agents for their workflows. Insights, examples, code.

Question

Stop Waiting for AI Tools: Build Your Own Development Agent

???? **Key Takeaway**: While everyone debates which AI coding tool to buy, 10x developers are quietly building custom agents that understand their unique workflows and solve their specific pain points.

At Info-Tech Live 2025, CEO Tom Zehren dropped a bombshell that should make every developer pause: "The 10x developers are building these modular, swappable architectures today. They create specialized bots for different tasks—research, analysis, visualization—then build orchestration layers that let them work together."

While most developers wait for the perfect AI coding assistant, elite developers are already building their own agents. They're not competing with Cursor or GitHub Copilot—they're solving problems these tools can't touch.

The 10x Developer Secret: Custom Agents for Custom Problems

Bob Garmaise, AI researcher at Info-Tech, put it perfectly during our interview: "You want to be very judicious about where you do it. Don't build an agent to code, because there are hundreds out there... But you know where the pain point is where it needs to be pointed."

That's the key insight. You shouldn't build an agent to replace Cursor (which just raised $900 million). But you absolutely should build an agent to handle your specific development workflow pain points.

⚠️ **Reality Check**: 90% of AI agent efforts fail to scale. But the 10% that succeed create transformative productivity gains for their creators.

What Makes a Development Agent Different from a Chatbot

The difference isn't just complexity—it's persistence and integration. As Garmaise explained: "The fundamental code difference is more in the memory and the tooling of the agent. How will the agent remember you next time you come by and there's a task in play? Will it remember to inquire about whether the task was completed?"

A chatbot answers questions. An agent remembers your project context, tracks your progress, and proactively helps you move forward.

The Three Pillars of Agent Architecture

According to the Info-Tech research, every functional agent needs:

1. Retrieval - Access to external knowledge and your internal documentation
2. Tools - Ability to take action in the real world through APIs
3. Memory - Context persistence across conversations and sessions

Real-World Agent Ideas That Actually Make Sense

Here are practical agents that developers are building right now:

Code Review Prep Agent

# Pseudo-code for a code review preparation agent
class CodeReviewAgent:
    def __init__(self, repo_path, style_guide):
        self.repo = repo_path
        self.style_guide = style_guide
        
    def prepare_pr(self, branch):
        # Analyze changes
        changes = self.git_diff(branch)
        
        # Check against style guide
        violations = self.check_style(changes)
        
        # Generate PR description
        description = self.generate_description(changes)
        
        # Suggest reviewers based on file ownership
        reviewers = self.suggest_reviewers(changes)
        
        return {
            'description': description,
            'reviewers': reviewers,
            'issues': violations
        }

Project Documentation Sync Agent

An agent that watches your code changes and automatically updates documentation, creates TODO tickets for missing tests, and notifies team members about API changes.

Development Environment Setup Agent

Instead of maintaining complex setup scripts, build an agent that can configure development environments based on project requirements, handle dependency conflicts, and even spin up necessary cloud resources.

The Technology Stack: Start Simple, Think Modular

When I asked Garmaise about the minimum viable tech stack, he recommended starting with cloud platforms: "For our clients, by and large, we're talking about the Azure Stack, or the AWS stack, which we see as being roughly equivalent. Frankly, I think AWS might be a little bit ahead in terms of how they packaged up some of their agent functionality."

???? **Recommended Starting Stack**: - **Cloud Platform**: AWS or Azure AI services - **Orchestration**: MCP (Model Context Protocol) - "the one we see referenced most frequently" - **Knowledge Base**: Graph RAG for connecting disparate information - **Memory**: Vector databases for context persistence

Why Graph RAG Matters for Developers

Garmaise shared a crucial insight about knowledge management: "With RAG, you can have a pretty good conversation about a very specific, constrained set of topics. But in technology and other spaces, you need to have conversations about all sorts of topics, and knowledge graph helps you link those disparate knowledge groups together."

For developers, this means your agent can understand connections between your codebase, documentation, issue tracking, and team communications.

Testing Your Agent: Beyond Traditional Unit Tests

Traditional testing approaches fall short with agents. As I discovered in my research, agent behavior varies based on context and learning. The solution? AI-powered testing.

Garmaise confirmed this trend: "One thing we heavily advise now is AI testing. So there's AI based testing tools out there... and more and more, as you would expect, of the testing is being driven by AI."

Testing Strategy for Development Agents

# Example testing approach for agents
class AgentTestSuite:
    def test_context_retention(self):
        # Test agent remembers project context
        agent.start_session("project-alpha")
        agent.process("Working on user authentication")
        
        # Simulate time gap
        time.sleep(300)
        
        # Agent should remember context
        response = agent.process("What was I working on?")
        assert "user authentication" in response.lower()
        
    def test_error_handling(self):
        # Test graceful degradation
        response = agent.process("Analyze malformed_file.json")
        assert response.includes_human_handoff()
        assert response.explains_limitation()

The Failure Prevention Playbook

The 90% failure rate isn't random. Bob Garmaise identified the top mistake: "I don't even think it's technical. I think the number one mistake is building versus buying."

???? **The Buy-First Strategy**: Before building any agent, thoroughly evaluate existing tools. Only build when you have a genuinely unique workflow that existing tools can't handle.

When to Build vs. Buy

Build When:

• Your workflow is unique to your organization
• You need deep integration with proprietary systems
• Existing tools miss crucial steps in your process
• You need specific memory/context that commercial tools don't provide

Buy When:

• Commercial tools handle 80%+ of your use case
• Your workflow is common across the industry
• You lack the engineering resources for maintenance
• Security/compliance requirements favor established vendors

Implementing Smart Fallbacks

One of the most valuable features? Knowing when to hand off to humans. Garmaise emphasized this: "Understanding the confidence level. Are you getting yourself into trouble? When do you need a human in the loop? I think is really a big part of the value."

class SmartHandoff:
    def __init__(self, confidence_threshold=0.7):
        self.threshold = confidence_threshold
        
    def should_handoff(self, task_complexity, confidence_score):
        if confidence_score < self.threshold:
            return True, "Low confidence in solution"
        
        if task_complexity > self.max_complexity:
            return True, "Task too complex for automation"
            
        return False, None
        
    def create_handoff_context(self, task, attempts, reasoning):
        return {
            'original_task': task,
            'attempts_made': attempts,
            'reasoning_trace': reasoning,
            'suggested_human_actions': self.suggest_actions(task)
        }

The MVP Mindset: Start Small, Think Big

Garmaise offered crucial advice about infrastructure investment: "I wouldn't even invest too much in the architecture yet. You're just think of this as your MVP kind of stuff we're playing right now. Don't invest too much in the infrastructure yet."

⚠️ **Architecture Warning**: The agent landscape is evolving rapidly. Build for learning and experimentation, not for long-term infrastructure investment.

This doesn't mean building throwaway code. It means focusing on solving real problems rather than creating perfect architectures.

Your Next Steps: From Reading to Building

1. Identify Your Pain Point: What repetitive task in your development workflow causes the most friction?

2. Evaluate Existing Tools: Spend a week testing current AI coding assistants against your specific workflow.

3. Start with Memory: Build a simple agent that remembers context about your current project. This alone provides immediate value.

4. Add One Tool Integration: Connect your agent to one system you use daily (Git, Jira, Slack).

5. Implement Smart Handoffs: Teach your agent when to ask for help rather than struggling alone.

The 10x developers aren't waiting for the perfect AI tool. They're building agents that understand their specific problems and integrate with their unique workflows. While everyone else debates which commercial tool to buy, they're already living in the future they created.

Your development workflow is unique. Your pain points are specific. Your agent should be too.

Comments

Is there one that is working that i can look through in github