Agentic engineering systems
We make coding agents reliable inside real codebases.
Agentic engineering systems for software teams: AI agents you own — customer service, voice, and sales — plus the context layer (AGENTS.md, CLAUDE.md, DESIGN.md), MCP and evals, and CI gates. 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
Four 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 agentic system
One build offer: an AI agent your business owns, scoped to the job. Customer service, voice, sales, knowledge, or internal ops — fixed scope, vendor-neutral, and you own all of it.
- Customer service, voice & sales agents
- Knowledge assistants (RAG) + internal ops
- AI Agent Rescue — fix one that failed live
Roll out AI coding for your team
The context layer, repo skills, MCP boundaries, evals, and CI agent-gates that make Claude Code, Codex, and Cursor reliable inside your real codebase. Training included.
- AGENTS.md / CLAUDE.md context layer
- MCP servers + CI agent-gates
- Team training to Org Scale
Keep it reliable as you scale
Models change, your codebase changes, and a system that worked at launch drifts. Managed Agent Operations keeps agents and their context layer current. Three-month minimum.
- Model upgrades + evals
- Monitoring + incident SLA
- Embedded scopes by application
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
One monthly retainer for teams with deployed agents — Managed Agent Operations keeps them current. Embedded scopes by application.
Senior-led operations: model upgrades, evals, monitoring, and a written monthly review. Three-month minimum, then monthly.
See the retainerLatest 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.
$5K · Credits 100% into any engagement within 30 days