AI agents are not replacing developers. They are replacing the work developers should not be doing.
Claude Code, OpenClaw, custom agents — I use AI agents as core team members, not novelty toys. Scaffolding, implementation, testing, customer discovery. The agents handle execution. I handle judgment.
Daily practice, not theory. Production code, not demos.
The agent ecosystem is bigger than one tool.
Different agents for different tasks. The skill is knowing which agent to deploy for which problem — and how to direct them effectively.
Agents for every layer of the stack.
Not just code generation. Customer discovery. Testing. Documentation. Monitoring.
Feature implementation
Claude Code builds features end-to-end — reading existing code, creating components, updating routes, writing tests. I provide architecture direction and review the output.
Customer discovery
OpenClaw agents monitor conversations across platforms, identify patterns, and surface product-market fit signals I would miss manually.
Security auditing
Automated security scanning for authentication, tenant isolation, and OWASP vulnerabilities. Agents check every route, every commit.
Multi-tenant architecture
AI agents navigate complex multi-tenant codebases — understanding site context, tenant isolation, and data boundaries.
Content intelligence
AI-powered monitoring, scoring, and organization of content from across the web. The system that powers BlackOps Center.
Workflow automation
Custom agent workflows for repetitive engineering tasks — migrations, pattern updates, documentation generation, and test scaffolding.
From the field. Real agent experience.
What happens when you take AI agents seriously as engineering tools.
The Future Dev Team: One Senior Engineer and an Army of AI Agents
The thesis — why the ratio of engineers to output is changing permanently, and what that means for team structure.
Read postMy First Week with OpenClaw: From Skeptic to Believer
What happens when you give AI agents real autonomy. A week-long experiment with OpenClaw that changed my view on agent capabilities.
Read postBuilding a Customer Radar with OpenClaw
Using AI agents for customer discovery — building a system that monitors real conversations to measure product-market fit signals.
Read postWhy This Feature Shipped in Hours (And Why Most Do Not)
The architecture that enables AI agents to ship features fast — and why most teams cannot replicate it without the right foundation.
Read postThe Industry Just Validated What I Have Been Building All Year
When a product leader described the AI capability overhang, every theme matched systems I had already shipped.
Read postCommon questions
What engineers ask about AI agents.
What is the difference between AI agents and AI assistants like Copilot?
Copilot suggests code completions — you are still typing. AI agents like Claude Code operate autonomously — they read your codebase, run commands, edit files, create commits, and build features end-to-end. The difference is like having someone whisper suggestions vs. having a developer on your team.
Are AI agents reliable enough for production code?
With the right guardrails, yes. Pre-commit hooks, type checking, linting, and human code review are non-negotiable. AI agents generate code fast — your quality gates need to match. I ship production code from AI agents daily, but every line goes through the same review process as human-written code.
How do you handle AI agents making mistakes?
The same way you handle any developer making mistakes — code review, testing, and rollback capability. AI agents make different mistakes than humans (they are confident about wrong patterns, they sometimes hallucinate APIs), so you learn to review for different things. The volume of output makes good review processes essential.
Which AI agent should I start with?
Claude Code if you want an autonomous coding agent with full codebase access. Cursor if you want AI-augmented editing. Start with one, learn its patterns, then expand. Trying five tools simultaneously leads to none being used effectively.
Can AI agents work on enterprise codebases?
Yes. My day job involves enterprise-scale commerce platforms. The key is providing good context — CLAUDE.md files, architecture documentation, clear naming conventions. AI agents work better on well-structured codebases. If your code is messy, clean it first.
Will AI agents replace developers?
AI agents replace tasks, not people. Writing boilerplate, scaffolding components, implementing known patterns, running checklists — these get automated. Architecture decisions, creative problem-solving, user empathy, and debugging novel problems remain human. The developer role shifts from typist to director.
Find out which agents fit your engineering workflow.
A 30-minute review of your current development process — where AI agents can replace manual work, which agents to start with, and how to structure the workflow that multiplies your output.
Daily agent practitioner. Production-proven workflows. Practical advice.
Schedule an Agent Workflow ReviewFree 30-minute review. Multi-agent experience. Production-tested.