Agentic Engineering.
Production-grade AI inside your product, not next to it. Claude as a peer rather than a chat box, with proper context budgets, prompt caching, and agent loops that actually finish their tasks. Where AI Business Automation targets your internal workflows, this targets the thing you ship to customers.
- 01User inputSlack message · web form · email
- 02Context fetchMCP · cached prompts · KB search
- 03PlanSonnet 4.6 · tool catalog
- 04Tool loopexec → observe → reflect
- 05Schema verifystructured output · zod
- 06Action / ReplyDB write · email · ticket
Six shapes I've shipped. Pick the one that matches your problem.
Not every workflow is an agent. The trick is picking the shape that fits, then locking the boundaries before writing a prompt.
Customer support copilot
Reads conversation context, drafts responses, routes the easy ones, escalates the hard.
Internal-ops agent
Replaces a 10-person manual workflow. Runs at midnight, queues exceptions for review.
Code review reviewer
Reviews PRs against a project skill (style, conventions, security) and comments inline.
Research / scrape agent
Background agent that pulls from public sources, normalizes, and writes to your DB.
On-product copilot (chat)
An in-product chat that knows the user's data and the actions they can take.
Workflow / agent platform
If your product IS the agent, Flowys-style. We build the runtime + the editor + the deploy.
architecture, hard reasoning, tricky debugging
everyday agent loops, code review, copilots
transformations, classification, background work
What's included.
- +Use-case shaping: where AI helps, and where it just adds latency
- +Model selection across the Opus, Sonnet, and Haiku tiers, with cost forecasting
- +Prompt caching, structured outputs, and tool-use loops
- +Skills-based agent design with long-lived, reusable specifications
- +MCP server integration for internal tools
- +Observability for tokens, cost, fail-rate, and escape hatches
What you can expect.
- ▶An AI feature you can defend in a board meeting, not a demo gimmick
- ▶A cost-per-task you understand and can budget against
- ▶An agent that improves month over month because the context budget is managed
How an engagement runs.
We pick one workflow, draw the data flow on a whiteboard, and decide what the agent should NOT do. The skill is in the boundaries.
Smallest possible loop. Real tools, real data, real failures. We watch token cost and edit the prompt with intent.
Caching, retries, schema-validated outputs, fallback to a smaller model, observability, and a kill switch.
Production deploy with metrics dashboards. Cost-per-task and fail-rate visible from day one.
Best for.
- ◇Teams whose first AI feature shipped and now needs to stop embarrassing them
- ◇Founders building an AI-native product (chat, agent, copilot) who want it solid
- ◇Internal tooling teams replacing a ten-person manual workflow with an agent
Engagement & pricing.
AI feature integration: 4 to 8 weeks. An AI-first product is scoped as full Product Engineering.
- Claude API (Opus / Sonnet / Haiku)
- Vercel AI Gateway
- AI SDK v6
- MCP servers
- Structured outputs
- Tracing
Recent projects in this lane.
Ready to start?
Send a one-paragraph brief.
What you're building, the rough timeline, and one constraint that matters. I read every message and reply within one business day.