Agents type. Engineers think.
Boilerplate, scaffolding, test generation, refactors - these are agent tasks. Architecture, trade-offs, product decisions, and code review remain human work. We have drawn the line clearly so quality does not drift.
how we work/AI-enabled engineers
AI does the heavy lifting. Our engineers make sure it's worth keeping
Every engineer, designer, and PM in our network works alongside AI agents as a matter of course - not as a differentiator, but as a baseline. The result is faster delivery without the sloppy output that defines most AI-assisted work. We don't optimise for token consumption. We optimise for outcomes.
Book a scoping call40%
faster delivery on comparable projects
500+
AI-enabled senior engineers in our network
2-4 weeks
from intro call to first sprint
7 years
minimum experience to join the network
Not vibe coding. Engineering with AI in the loop.
There is a version of AI-assisted development that ships fast and breaks everything quietly. Autocomplete runs loose, agents generate plausible-looking code, nobody reviews it carefully, and the debt compounds until the next engineer inherits a codebase nobody fully understands. That is not what we do. Our engineers treat every line of AI-generated output the way they treat a junior engineer's first draft - useful, but never final. Every line passes through the same gates as hand-written code: human review, automated tests, type checks, linting, and security scans. Agentic does not mean unreviewed. The engineers in our network have the experience to know what good output looks like, catch the subtle ways AI goes wrong, and push back when an agent is generating more code than the problem actually requires. That judgment is what the model cannot replace - and it is what our clients are paying for.
Our principles - what separates AI-augmented engineering from AI-generated shortcuts.
Our process - end to end
Every phase from discovery to deployment is AI-augmented. Here is what that looks like in practice across a typical engagement.
1. Discovery & product definition
PMs synthesise competitive research, user interview findings, and stakeholder call transcripts using Claude and ChatGPT. Notion AI structures specs and keeps documentation searchable. Read.ai captures and transcribes every stakeholder call, surfacing decisions and action items automatically - so nothing agreed in a meeting disappears into someone's notes.
2. Design
Designers work in Figma with Figma Make and Figma AI for rapid variant generation, auto-layout suggestions, and copy refinement. Claude Code turns concepts into working prototypes in hours, not days. Design tokens flow into code through Tokens Studio - closing the gap between design intent and engineering output.
3. Architecture & planning
Tech leads use Claude and Cursor in agent mode to draft architecture diagrams, evaluate trade-offs, and break epics into actionable tickets. Decisions are documented as Architecture Decision Records the moment they are made. No tribal knowledge, no context lost when an engineer rotates off the project.
4. Implementation
Our engineers move fluidly between four modes. Agentic mode: letting Claude Code, Cursor, or Windsurf drive multi-file changes and scaffolding autonomously. Spec-driven development: writing clear specs and tests first, then letting agents implement against them. Multi-agent workflows: parallel agents on independent tasks to compress timelines. Hands-on coding: when the problem is novel, the system is sensitive, or the stakes are high, our engineers write it themselves. GitHub Copilot handles inline suggestions across all modes.
5. Review & quality
When agents increase output volume, code review becomes the bottleneck. We have redesigned ours to keep up. CodeRabbit and Graphite do a first pass on every PR before a human opens the diff. PRs are kept small and atomic. Snyk and Semgrep catch security issues. Human review still gates every merge - focused where humans add the most value.
6. Deployment & observability
CI/CD runs through GitHub Actions with AI-assisted failure triage. Sentry, Datadog, and PostHog surface issues fast, with AI summarisation cutting through alert noise. Incident retrospectives are drafted by agents and refined by the team - so the learning is captured, not just the fix.
Your stack is ready for AI. The question is whether your engineers are.
Tell us where you need capacity. We'll find you senior engineers who've been working this way for years - not just since it became a trend.
Book a free call
"We couldn't afford a 6 month hiring cycle for rare AI skills. Devspace had specialists embedded within 2 weeks.
Lauren Pedersen — CEO of SportAI
DevSpace engineers adapted fast to our needs, our culture, our priorities. Self-directed and focused on what mattered most to our team.
Kim Stian Haugland — VP Operations and Technology of Optimarin
Every DevSpace engineer understood our stack, fitted our process, and added real value fast. That consistency is rare and genuinely makes a difference.
Andrew Page — CEO of Hippo Manager
The AI stack, by role
The AI layer is woven into every discipline - not bolted on top of it. Every role in a Devspace engagement has a defined AI stack they use daily: specific tools, specific use cases, and clear boundaries on where human judgment takes over. Here is what that looks like in practice across a typical team.
Primary tools: Cursor · Claude Code · Windsurf · GitHub Copilot · CodeRabbit · Linear
Move fluidly between agentic mode, spec-driven development, and multi-agent workflows. Inline suggestions from Copilot across all contexts. CodeRabbit runs the first PR review pass before a human opens the diff.
What this means for you
Shorter cycles, same quality bar
AI handles the parts of the work that scale linearly with volume - scaffolding, boilerplate, test generation, documentation. Your engineers spend their hours on the parts that require judgment. The output is the same quality; the time to get there is measurably shorter.
No hidden AI debt
The most expensive AI code is the kind nobody reviewed before it merged. Every line our engineers ship - whether they wrote it or an agent did - goes through the same quality gate. You will not find a surprise rewrite requirement six months later because agentic output ran unchecked.
Senior leverage, not junior volume
AI amplifies what is already there. A senior engineer with AI ships work a junior with the same tools simply cannot. Our network is senior by default - 9.3 years average experience - which means the AI leverage compounds on top of real craft, not around the absence of it.
Responsible AI usage
Working with AI seriously means working with it carefully. Four things we do not compromise on.
The right model for the right task
We do not default to one model for everything. Heavy reasoning, architecture, and refactoring go to the strongest available models. Fast, high-volume work - autocomplete, classification, summarisation - uses lighter models where they perform just as well. For sensitive or regulated workloads we use locally-hosted or private-deployment options. Each task gets the model that is appropriate, not the most convenient.
Data security and access boundaries
Client code and data only go to model providers with enterprise-grade agreements: zero data retention, no training on inputs, audited infrastructure. We use Claude for Work, ChatGPT Enterprise, and equivalent tiers - never consumer accounts for client material. Secrets, credentials, and PII are filtered out of prompts by tooling, not goodwill. Agents run in sandboxed environments with scoped access.
GDPR and data residency
For European clients and EU personal data, we use EU-region model endpoints with verified GDPR-compliant data processing agreements. Personal data is minimised before it reaches any model - stripped, redacted, or pseudonymised wherever possible. Data subject rights are honoured in our prompt logs and agent memory stores the same way they are in any other system.
Human oversight on consequential decisions
Agentic workflows can move fast enough to make consequential decisions before anyone notices. We do not let that happen. Architecture choices, security-sensitive changes, data migrations, and anything touching client infrastructure require a senior engineer to review and explicitly approve - not just pass a linter. Speed is not a reason to remove the human from decisions that are hard to reverse.
Ready to work with engineers who use AI properly?
Whether you need AI-enabled senior engineers embedded in your team, a Fractional CTO, or an honest look at where your technology stands - most engagements are scoped in two days and underway within two to four weeks.
Or email us directly at post@devspace.no to get a free consultation.