Agents code alongside us. Not instead of us.
We ship production systems with coding agents working in parallel — Claude Code for architecture, Codex CLI for quick iteration, Gemini CLI for huge context, Snowflake Coco for data-native work. Senior engineers driving, agent teams executing.
Coding agents won't replace your engineers. They'll compound the ones who know how to drive them.
The skill isn't prompting. It's orchestration: which agent does which part, in what order, with which context, under what review.
Four skills, not one
Build and deploy AI applications. Software engineering fundamentals. Using coding agents. Shaping the build — deciding what belongs in the spec. Teams that have only the third one ship fast and cannot tell you whether it works.
Underneath all four sits the discipline that separates production from demo: evals and error analysis.
Why measurement, not trust
AI applications differ from every other kind in one way: the output is unpredictable. That is the whole reason evals exist, and the reason we leave you with them.
Every engagement hands back the evals, the error analysis, and the review habits — not just the code an agent wrote.
Four coding agents. Each for what it's best at.
Pick-one-tool people lose. Every agent has a sharp edge and a dull one. We know both, and we route work accordingly.
Claude Code
Architecture, complex refactors, multi-file changes. Holds a plan across a large codebase and honours house style.
Strengths: reasoning about system design, long-context code comprehension, agentic workflows, file-aware edits.
Sharp edges: slower on simple one-liners; can over-architect if not constrained.
Codex CLI
The tight loop: write, run, read the error, try again. Bash-native and fast.
Strengths: shell-native workflow, tight feedback loops, raw speed on small tasks.
Sharp edges: weaker at holding a large architecture in its head; use Claude Code for that.
Gemini CLI
A million-token context for codebase-wide passes: lint a whole repo, plan a migration, summarise a legacy system in one shot. Also our multimodal reach.
Strengths: enormous context window, multimodal (image/video), Google ecosystem (GCP, BigQuery).
Sharp edges: less precise on local file edits than Claude; use it to analyze, Claude to edit.
Snowflake Coco
A coding agent that speaks warehouse natively: Snowflake, SQL, pipelines, semantic layers.
Strengths: warehouse-native, no copying data to the model, understands cost/performance trade-offs.
Sharp edges: narrower scope — it's for data work, not general engineering.
Orchestration is the skill. Four patterns we rely on.
Do N things at once.
When a task has 3+ independent pieces — "refresh screenshots across all products," "lint every repo," "critique these landing pages" — we spawn one agent per piece, in parallel. A senior engineer orchestrates. Elapsed time goes from hours to minutes.
The agent only knows what you tell it.
A shared memory layer (CLAUDE.md, wiki, plan files) so every agent starts with the same mental model. Nobody re-explains the codebase.
Review is the bottleneck — so make it fast.
Agents write, humans review. Small diffs, atomic commits, focused PRs. Review speed is the real constraint on agentic throughput, so we engineer for it, and a senior engineer signs every merge.
Different agent for different work.
Different agent for different work. Switch by task, not by habit.
What a quarter looks like.
Shipped systems with real users — not benchmarks, not demos.
Legacy rewrite: months → days
A focused team on Claude Code and Codex CLI rebuilds what an enterprise team needed months for — cleaner architecture, sharper UI — in days.
8 shipped products · 1 quarter
Undervolt, RefereAI, Sideline, StudyPal, CoachClaw, HD Research, Studio Copilot, Homenest. Eight live products, eight active user bases — not eight prototypes. See the work →
Small teams beat big armies.
One giant agent army doesn't work. Small, sharp sub-teams do. Make one group agent-native first; the gains pull the rest of the org along.
Multi-agent workflows in real production.
Agents wired into revenue-bearing systems, working alongside humans, scoped and evaluated end-to-end. Not pilots, not science projects.
Three engagements, one goal: your team ships faster without shipping slop.
Where are agents real leverage?
We sit with your engineering team, map your workflow, and identify the 3–5 places agentic coding actually wins — and the places it doesn't. Honest report, no sales push.
Install the stack that fits
Claude Code, Codex, Gemini CLI, Coco — whichever match your workload. CLAUDE.md conventions, shared memory, agent-ready templates, review workflows. We don't just install tools; we install the habits.
Work alongside your team
We join your repos and build with your engineers for a sprint. Show, don't tell. By the end, your team has the muscle memory to run agents without us.
Prefer self-serve? The full 47-lesson Agentic Engineering programme is the same practice, taken at your own pace.
Your team should be using agents. Most don't know how.
Free 30-minute call. We'll show you what changed for us — and whether it's the right call for your team.