AI Agent Development and Workflow Automation
Practical AI agents and automation that connect to your real business systems.
Overview
Generic chatbots rarely move the needle. Useful AI agents do specific work: qualify leads, summarize tickets, draft proposals, route incoming emails, extract data from documents, and trigger downstream actions in tools you already use. I design and ship agentic systems that integrate cleanly with your stack and stay reliable in production.
I create practical AI agents and automation workflows that connect to your website, CRM, forms, documents, APIs, and internal tools. The goal is useful business automation, not novelty.
Key features
What is included.
Lead and intake agents
Qualify inbound leads, score them, and route the qualified ones into your CRM or inbox in real time.
Content and research workflows
Long-form drafting, document summarization, research assistants, and editorial pipelines under human review.
Multi-step tool calling
OpenAI and Claude function calling, structured outputs, retrieval, and orchestrated multi-step reasoning.
Human-in-the-loop guardrails
Approval checkpoints, audit logs, and fallback paths for any action that touches real customer or financial data.
Reliable error handling
Timeouts, retries, structured logging, and observable failure modes — not a black box that breaks silently.
Server-side keys and secrets
API keys stay on the server, requests are rate-limited, and prompts are stored with versioning and review.
Benefits
Why this matters.
- Hours back per week
- Automate repetitive review, triage, and drafting work so your team focuses on judgment calls and customer relationships.
- Faster response times
- Inbound leads, support emails, and operational events get an immediate, structured first response.
- Connected to your real data
- Agents query your CRM, knowledge base, billing system, and product database — not a static FAQ.
- Safer than DIY prompts
- Production-grade input validation, output schemas, and observability built in from day one.
Process
How the work runs.
Use case discovery
Identify a narrow, valuable task that an agent should own end to end.
Data + integrations
Map sources, APIs, and any human review steps that need to stay in the loop.
Prototype
Build a working v1 with the real prompt, real data, and real tools — measurable from day one.
Harden
Add evals, guardrails, monitoring, and structured outputs before exposing to live users.
Iterate
Tune prompts, expand scope to adjacent tasks, and report measurable impact each cycle.
Technologies
Tools I use.
Battle-tested choices for production work. Always picked to match the team and existing systems.
- OpenAI API
- Claude API
- Anthropic SDK
- LangChain
- Vercel AI SDK
- Node.js
- TypeScript
- Pinecone
- Supabase
- Zapier
- n8n
FAQ
Frequently asked questions.
- Which AI provider do you build on?
- Anthropic Claude and OpenAI are the defaults, chosen per use case. I can also work with open-source models hosted on AWS, Azure, or your own infrastructure when data residency demands it.
- Will my data be used to train models?
- No. Production agents I build use API endpoints that contractually exclude training on your data, and sensitive content can be routed through self-hosted models if required.
- Can the agent take real actions, like sending emails or updating records?
- Yes, with explicit guardrails. Destructive or irreversible actions go through human approval steps, audit logs, and rate limits before reaching production.
- How do you measure whether the agent is working?
- Every project ships with success metrics, evaluation prompts, and a small benchmark set. We track quality, cost per task, and intervention rate so improvements are visible.
Related services
Explore more.
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Automate the work that actually matters
Tell me the task you would automate first and the system it would touch. I will sketch the smallest valuable agent and what it would take to ship.