Mid-2024. A colleague is getting coffee. An agent, no human in the loop, closes a bug ticket, writes the fix, opens a PR, requests review. He came back with his cup and the work was done. That's when I stopped calling this "fancy autocomplete."
We've been running agentic workflows in actual client codebases since then. Finance, logistics, SaaS. Not sandbox demos, production code, real pressure, real consequences when something goes wrong. And things do go wrong. That's part of what we learned.
What an agent actually is, and isn't
Not a chatbot. Not a smarter search. An agent reads your codebase, checks your tickets, looks at CI state, then acts, writes files, calls APIs, runs tests, until the task is done or it hits something it needs a human to resolve. That last part matters more than most people talk about.
Approval gates aren't optional. Especially in regulated industries. An agent that can do anything without asking is a liability, not an asset. We've had to re-architect pipelines around this after the fact. Learn from that.
The thing that actually moved the needle
Not code generation. Code review.
Automated review agents, catching routine issues before a human even opens the PR, cut review cycles by 60–80% across three EU clients in 2025. PRs that sat for three days got same-day feedback. The developers didn't notice it at first. Then they noticed they weren't context-switching back to stale reviews anymore. That's the kind of change that compounds.
What else changed, honestly
Legacy work got faster. We had a 12-year-old ERP codebase at one client, nobody there fully understood the billing module. Agents mapped the dependencies in two days. A human team would have taken three weeks just to feel confident enough to touch it. Then we used the map to plan the migration. 40–60% time compression versus what we'd have quoted without agents.
Specs became the bottleneck. That was unexpected. When an agent can scaffold a feature from a well-written spec in hours, the thing slowing you down is no longer "write the code." It's "write a spec precise enough that the agent doesn't go sideways." Business analysts became critical almost overnight. Nobody predicted that.
Testing improved without anyone making it a priority. Agents generate test suites from source, pull edge cases from bug history, run regressions after every commit. On data pipelines we extended this to schema validation, catching upstream drift before it becomes a 2am incident.
Where RSC fits in
We've built our entire delivery process around this. Not as a feature to demo. That\'s how the work gets done. Agentic pipelines across requirements, implementation, review, and ops. Humans at every checkpoint that matters.
If you're evaluating where to bring agents into your own delivery: the questions that matter aren't "which model is best." They're whether your specs are tight enough, whether your repo has enough context, and whether your team knows which decisions to keep for humans. We're happy to talk through what that looks like for your context.
🚀 RSC delivers AI-powered custom software for European enterprises. Not theory, production systems. Talk to us.