When Anthropic's Claude Code landed on our radar, we were skeptical — another AI tool promising to change everything. Six months in, our skepticism has turned into a competitive advantage we're actively building on. Here's the honest account of what it's done for our practice, how we use it day-to-day, and what other agencies should know before they adopt it.
The Problem We Were Actually Trying to Solve
Like most agencies, we were caught in a familiar squeeze: client expectations were rising, project timelines were tightening, and our dev team's capacity wasn't scaling fast enough to match. We'd experimented with GitHub Copilot and a handful of other AI assistants, and while they helped at the margins, they didn't fundamentally change how our team worked. They were autocomplete on steroids — useful, but not transformative.
What we needed wasn't faster typing. We needed faster thinking. We needed a tool that could understand an entire codebase, reason about architecture decisions, write tests, refactor legacy code, and help us ship cleaner work in less time. That's a very different ask.
We didn't need faster autocomplete. We needed a collaborator that could hold the whole project in its head.
What Claude Code Actually Is
Claude Code is Anthropic's agentic coding tool that lives in your terminal. Unlike in-editor assistants, it operates at the level of your entire project — it can read files, edit them, run commands, manage git workflows, and reason across your whole codebase. Think of it less like a text editor plugin and more like a deeply capable collaborator who has read every line of your repo and is ready to work.
One of the things that genuinely surprised us was how natural the interaction model feels. You describe what you want in plain language, and Claude Code works through it methodically, explaining what it's doing and why, and asking for confirmation before taking any irreversible actions.
How We Integrated It Into Our Workflow
Phase 1 — Discovery & Codebase Orientation
Every new client project starts with a discovery phase where our devs need to get up to speed on an existing codebase fast. Historically, this took days. Now, one of the first things we do is point Claude Code at the repo and ask it to map the architecture. It surfaces how modules connect, where the critical paths are, what tech debt looks like, and what decisions the previous team made. In our experience, this alone cuts codebase onboarding time by two-thirds.
We've built a standard CLAUDE.md file — Claude Code's project memory file — that we populate at the start of every engagement. It captures client-specific context: their tech stack preferences, coding standards, deployment environment, and any constraints we've identified. This context persists across every session, so Claude Code never has to be re-briefed.
Phase 2 — Active Development
In active development, Claude Code has become our default starting point for any non-trivial feature. We use Plan Mode extensively — a mode where Claude maps out exactly what it intends to do before touching a single file. This is critical in an agency context where you're working in client codebases you didn't originally build. The plan step lets a senior dev review the approach before any execution happens, keeping quality control tight without slowing things down.
Our devs have also embraced parallel sub-agent workflows for larger features. Claude Code can spin up multiple agents working simultaneously — one handling the backend API layer while another scaffolds the frontend components and a third writes tests. What used to take a full sprint can often be done in a day.
Phase 3 — Code Review & Quality Assurance
One use case we didn't anticipate being so valuable: using Claude Code as a pre-review pass before code goes to a human senior developer. We ask it to review a PR diff and flag anything — logic errors, security concerns, performance issues, missing edge cases, inconsistent patterns. It catches a surprising amount that might otherwise slip through, which means our senior devs spend their review time on higher-level architectural questions rather than hunting for issues.
What Surprised Us
The good surprises
The depth of reasoning caught us off guard. This isn't a tool that produces plausible-sounding code that breaks in edge cases. When working with complex systems — multi-service architectures, legacy systems with years of accumulated debt, intricate data models — Claude Code reasons about dependencies and side effects with a level of care that feels genuinely expert. It doesn't just do what you asked; it flags when what you asked might cause a problem.
The honest caveats
Cost management requires attention. Claude Code usage adds up, and without awareness of how token consumption works, a complex project can generate significant API costs. We've gotten disciplined about session hygiene — breaking large tasks into focused sessions rather than letting single threads balloon.
It also works best with developers who can review its output critically. This isn't a tool for handing the keyboard entirely to AI. The teams getting the most out of it treat it as an expert pair programmer — one who needs direction, whose work needs review, and whose most important contribution is the cognitive horsepower it brings to initial thinking.
Treat it like an expert pair programmer — not an autopilot. The agencies winning with AI are the ones who stayed in the driver's seat.
Practical Advice for Agencies Considering Adoption
- Start with a single project type. Don't try to use Claude Code for everything simultaneously. Pick one type of project where you can measure before/after clearly, get your workflow right, then expand.
- Invest in your CLAUDE.md files. The quality of your project memory files will determine a large part of the quality you get out. Agencies that spend 30 minutes building a thorough context file at project kickoff get dramatically better results.
- Build custom slash commands for your agency workflows. Claude Code supports custom commands that automate repeatable processes. We've built commands for our standard project kickoff checklist, our accessibility audit process, and our deployment preflight — turning tribal knowledge into repeatable, AI-executable procedures.
Where This Is Heading
We're still early in the agentic development era, and Claude Code is evolving quickly. The near-term direction we're watching closely: smarter multi-agent coordination, deeper integration with project management tools, and more sophisticated spec-driven development workflows that connect client briefs directly to implementation plans.
For agencies, the strategic opportunity is clear. AI-powered development isn't going to replace the need for strong strategy, smart design, and senior engineering judgment. But it is going to change the economics of delivery. Agencies that figure out how to pair great human thinking with capable AI execution will be able to do more, at higher quality, for more clients — without proportionally scaling headcount.
That's not a threat to the agency model. It's the best version of it.