I'm Justin Holt — an AI-first developer who helps teams and organizations adopt AI tooling, build autonomous agents, set up MCP servers, and integrate AI-powered workflows into their development process.
I work with teams to evaluate, implement, and optimize AI development tooling — so you can ship faster and build smarter.
I'll evaluate your team's needs and set up the right AI coding tools — Claude Code, Cursor, Copilot, or a combination — configured for your codebase and workflow.
I build custom AI agents that automate real work — from code review bots and deployment assistants to complex multi-agent systems tailored to your business.
I build and deploy MCP servers that connect your AI tools to databases, APIs, and internal systems — giving your models the context they need to be truly useful.
I run hands-on workshops that teach your team prompt engineering, AI-assisted coding patterns, and how to get the most out of every tool in the AI stack.
Not sure where to start? I help teams assess their current workflow, identify high-impact AI opportunities, and build a roadmap for adoption that makes sense.
I set up CLAUDE.md files, .cursorrules, project documentation, and structured context systems that make AI output dramatically better for your specific codebase.
I stay hands-on with the best AI coding tools so I can recommend and set up the right ones for your team.
Anthropic's agentic CLI that lives in your terminal. Reads your codebase, runs commands, edits files, and commits code — all through natural language.
AI-native code editor built on VS Code. Features tab completion, inline editing, multi-file chat, and an agent mode that writes across your entire project.
The original AI pair programmer. Inline suggestions, chat panel, and Copilot Workspace for planning and implementing changes across repos.
AI-powered IDE with "Cascade" flows that chain multi-step coding tasks. Deep codebase awareness and automatic context retrieval for complex edits.
Open-source AI pair programming in your terminal. Git-aware, multi-file editing, and works with any LLM. The power-user's choice for AI-assisted coding.
Generative UI tools that turn prompts into production-ready frontend code. Describe what you want, get a working component with a live preview instantly.
I help teams adopt these proven patterns for integrating AI into every stage of the development lifecycle.
The modern dev loop: break work into clear tasks, write precise prompts, let the AI generate code, review and iterate, then ship. Works best when you treat AI as a junior dev who needs clear specs.
Write your tests first, then let AI generate the implementation. The test suite becomes the spec. AI coding assistants excel when they have concrete pass/fail criteria to work against.
Use AI to review PRs for bugs, security issues, and style violations before human review. Catches common mistakes instantly and lets human reviewers focus on architecture and logic.
The art of feeding AI the right context. CLAUDE.md files, .cursorrules, code comments as prompts, and structured project documentation that makes AI output dramatically better.
I build production agents using these frameworks and patterns — from simple tool-calling bots to complex multi-agent systems.
Anthropic's official framework for building production agents with tool use, guardrails, and multi-turn conversations.
Composable chains and stateful graphs for building complex agent workflows with memory, tools, and branching logic.
Multi-agent orchestration where specialized agents collaborate on tasks with defined roles, goals, and delegation patterns.
Lightweight framework for building agents with handoffs, guardrails, and tracing built in.
Agents that interleave reasoning with tool calls — think step-by-step, then act on each thought.
Giving LLMs access to APIs, databases, file systems, and external services through structured tool definitions.
Agents that create plans before executing, and reflect on results to self-correct and improve over iterations.
Escalation patterns where agents request human approval for high-stakes actions — the safety net for production agents.
I teach and implement these techniques to get consistently better results from any model — the skill that separates good AI outputs from great ones.
Define persona, constraints, and output format upfront. The system prompt sets the stage for everything that follows — treat it like configuring a tool.
Show, don't tell. Providing 2-3 examples of desired input/output pairs dramatically improves consistency and accuracy for structured tasks.
Ask the model to think step-by-step before answering. Breaks complex reasoning into manageable chunks and dramatically reduces errors.
Request JSON, XML, or markdown-formatted responses. Constrained output formats make AI responses programmatically parseable and reliable.
Break complex tasks into sequential prompts where each step's output feeds the next. Decomposition beats trying to do everything in one shot.
Use AI to write and refine prompts. Ask one model to generate the optimal prompt for another — recursive improvement for complex use cases.
I build and deploy MCP servers — the open standard that connects your AI tools to databases, APIs, and internal systems.
Build servers that expose tools, resources, and prompts to any MCP-compatible client. Connect AI to databases, APIs, file systems, and custom business logic.
Claude Desktop, Claude Code, Cursor, and other AI tools that speak MCP natively. One protocol to connect them all — no custom integrations needed.
Beyond tools — MCP serves contextual data and reusable prompt templates. Give AI access to documentation, schemas, and domain knowledge on demand.
The models, platforms, and infrastructure I work with and help teams evaluate for their specific needs.
The resources I use and recommend to clients for staying current in the fast-moving world of AI development.
Official docs from Anthropic, OpenAI, and the MCP specification. Start here for accurate, up-to-date technical references.
Latent Space, Practical AI, The AI Breakdown, and conference talks from leading AI researchers and practitioners.
GitHub repos, starter templates, MCP server examples, and community-built tools. Fork, learn, and contribute back.
Curated collections of effective prompts for coding, analysis, writing, and agent systems. Learn from what works.
SWE-bench, HumanEval, MMLU, and custom eval frameworks. Measure model performance objectively before choosing your stack.
Discord servers, Reddit communities, and X/Twitter spaces where AI developers share techniques, debug together, and stay ahead of the curve.
I'm Justin Holt — a developer with 27+ years of experience who went all-in on AI-first development. I've built my entire workflow around AI tooling and now I help other teams do the same. Whether you need hands-on setup, custom agent development, or training to get your team up to speed — I bring deep practical experience with every major AI development tool on the market.
Ready to adopt AI development tools? Need help building agents or setting up MCP servers? Let's talk about your project.
