I help technical teams adopt AI-first development workflows and build production-grade agentic systems. Two different services, one shared principle: if it's not safe enough for production, it's not done.
Your developers already use Claude Code and Cursor. They generate features, refactor modules, fix bugs in synchronous sessions. That's Level 1. The real shift is Level 3: multiple autonomous agents picking up issues from the backlog, opening PRs, generating proof that the fix works, and waiting for a human to review. You can run async agents without the right infrastructure. The results just won't be good enough to ship. Getting to Level 3 requires building trust: the confidence that agent output is reliable enough for production. Three enablers, working together, create that trust.
CI/CD pipelines that run on every PR. Unit tests, integration tests, end-to-end tests with Playwright. Containerized environments with Docker so agents and developers work in identical setups. Code quality gates that keep the codebase navigable for both humans and AI. Without these foundations, letting autonomous agents loose on your codebase is a liability.
Agents can read code, but they can't read minds. Business rules, compliance constraints, formatting conventions: everything unspoken needs to be written down in structured skills that reach into every module, every convention, every business rule. Not just a README. A capillary knowledge base that gives agents the same institutional knowledge a senior developer carries in their head.
When an async agent completes a task, the engineer reviewing it needs more than a diff. For frontend changes, the agent generates a video walkthrough. The engineer watches it on their phone and evaluates in seconds, without pulling the branch. Complementary to automated testing: CI catches regressions, the video handles the human judgment layer.
None of these is sufficient alone. Foundations without a knowledge base means agents that pass CI but miss business requirements. A knowledge base without observability means you can't verify what agents actually did. All three together create trust. Trust enables Level 3: a team of async agents you can actually rely on.
I audit your current development workflow. Where are the gaps that would make autonomous agents risky? Missing tests, no CI pipeline, manual deployments, undocumented business rules? I map out which trust enablers are missing and what needs to change before agents can be relied on.
I set up (or upgrade) the three enablers: CI/CD with automated testing, containerized environments, staging for safe validation, a capillary knowledge base that translates unwritten rules into agent-readable skills, and observability pipelines for agent output verification.
I introduce agents into the team's workflow, starting at Level 1 (synchronous sessions for architecture and complex features) and progressing to Level 2-3 (autonomous agents handling bug fixes, small features, and maintenance in parallel). I configure the orchestration layer and establish review processes for agent-generated PRs.
The goal is that I leave and your team keeps going. They have the trust infrastructure in place. Async agents pick up work from the backlog. CI catches mistakes. The knowledge base keeps agents grounded. Observability lets engineers verify results quickly.
I don't build individual chatbots. I design agentic systems: multi-component architectures where AI agents interact with your existing tools, data, and processes. Every system comes with guardrails, compliance considerations, and observability into what the agents are doing and why.
Real-time voice assistants that integrate with your existing systems. Cascade pipelines with full observability into transcription, reasoning, and speech synthesis. I've built production voice assistants handling appointment booking, CRM queries, and multi-system orchestration.
Workshop booking case studyMulti-agent systems that generate and execute complex workflows from natural language. Designed for domains where auditability matters: every step is logged, every decision is traceable, every execution is reproducible.
Workflow builder case studyAI-powered data processing that transforms raw data into actionable insights. From financial portfolio analysis to CRM data enrichment, with the AI surfacing patterns and risks that manual analysis would miss.
AI systems that connect to your existing stack: Google Workspace, Slack, WordPress, CRMs, ERPs, and domain-specific platforms. I build the bridges between your tools and AI capabilities, not throwaway demos.
Blockchain agent case studyI work on short-term contracts (1-6 months), 2-4 days per week, remotely with clients worldwide.
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