Agentic engineering systems
We make coding agents reliable inside real codebases.
Agentic engineering systems for software teams: the context layer (AGENTS.md, CLAUDE.md, DESIGN.md), MCP and evals, CI gates, and custom AI agents — vendor-neutral across Claude Code, Codex, Cursor, and Gemini.
Evals, observability, and handover built into every engagement.
Field notes from building AI systems.
Build logs, working blueprints, and what shipped — weekly.
Coding agents are easy to start. Reliable inside a real codebase is the hard part. A demo agent works once on a toy repo. In your codebase it needs context the model can't guess, guardrails, evals, and a human-in-the-loop path — or it ships confident, wrong changes.
Agents that survive production need a context layer (AGENTS.md, CLAUDE.md, DESIGN.md), repo-specific skills, MCP boundaries, evals and regression tests, approval gates, observability, and a team that knows how to run them.
Where to start
Three steps, fixed scope, senior-led. Most teams begin with the audit and move down the path. Pricing lives on the services page; the exact number gets scoped on a 30-minute call, never guessed at.
Start with a readiness audit
Five days inside your codebase, docs, tests, and agent workflow. You leave with a scorecard, the gaps that matter, and a 90-day roadmap. It credits 100% into whatever you build next.
- Agentic-readiness scorecard
- Context-layer + eval gap lists
- 90-day roadmap, fixed price
Build the system, or the agent
The context layer and SDLC rollout that make coding agents reliable, or a custom AI agent that runs a real workflow in production. Fixed scope, vendor-neutral, and you own all of it.
- Agentic Repository System + SDLC Rollout
- Custom AI Agent (customer-service / voice)
- MCP servers + RAG pipelines
Keep it reliable as you scale
Models change, your codebase changes, and a system that worked at launch drifts. We keep agents and their context layer current, as managed ops, an embedded engineer, or a fractional lead.
- Managed Agent Operations
- Embedded AI Engineering Pod
- Fractional Head of AI Engineering
A customer-service or voice agent you own — not one you rent.
Built on your data, your channels, and your guardrails — with retrieval, tools over MCP, evals, and a human-in-the-loop path. A system you own and extend, not a black-box SaaS seat.
An agent that runs a real workflow — not a demo.
Every integration point, guardrail, and eval is built in, so it survives real users instead of the happy path.
resolved · confidence 0.96
The technical stack
Model providers, agent orchestration, MCP, RAG, observability, and the app stack we run for funded teams.
Model providers
OpenAI · Claude · Gemini · Grok
Agent orchestration
LangGraph · OpenAI Agents SDK · Vercel AI SDK · Custom orchestration
MCP
Model Context Protocol · Custom MCP servers · Tool registries · Private tools
RAG
Postgres / pgvector · Supabase · Pinecone · Weaviate · Hybrid search · Reranking · RAG
Observability
Langfuse · LangSmith · OpenAI tracing · Custom dashboards
App stack
Next.js · React · TypeScript · Python · Supabase · Postgres · Redis · Cloudflare · Vercel
How we make agents reliable
Six steps from first audit to ongoing operations — the repeatable path that keeps coding agents reliable inside your codebase.
Reference systems, not slideware
Internal reference systems and prototypes that show how we build — architecture, logging, evals, and the failure modes we design for.
AI Ops Dashboard
A control layer for agent runs, costs, evals, approvals, and failures.
RAG Pipeline
Ingestion to hybrid retrieval to reranking to cited answers.
Production Agent
A bounded LangGraph agent with explicit state, tools, and approvals.
Claude Code Workflow
Repo instructions, skills, MCP, and a GitHub Actions review loop.
MCP Server
A TypeScript MCP server exposing internal tools behind auth.
Model Routing System
Routes tasks across OpenAI, Claude, Gemini, and Grok with fallback and cost control.
Keep it running
Monthly retainers for teams with deployed agents — Managed Agent Operations, an embedded pod, or a fractional head of AI.
Senior-led operations, an embedded pod, or a fractional head of AI engineering. Cancel any time, no lock-in.
See all retainersManaged Agent Operations
Keep your agents and context layer current — new skills, model upgrades, evals, and quality monitoring, every month.
Embedded AI Engineering Pod
A senior AI engineer embedded in your team, shipping agentic systems in your repo.
Fractional Head of AI Engineering
Senior leadership for your AI engineering — strategy, architecture, and team mentoring.
Latest posts
Build logs, agentic engineering decisions, agent failures, and what survives real users.
Rolling out coding agents? A 5-day Agentic Readiness Audit turns your codebase, docs, tests, and agent workflow into a 90-day roadmap with fixed prices. It credits 100% into any follow-on within 30 days.
$3.5K · Credits 100% into any engagement within 30 days