Agentic systems for software teams.
Implementation patterns, deployment guides, and governance strategies for AI coding agents, autonomous workflows, and AI employees in engineering organisations.
Agentic systems. AI that can take actions, make decisions, and execute multi-step workflows. Are moving from research demos to production deployments, for engineering teams, this means coding agents that write and test code, review PRs, and automate repetitive development tasks.
The challenge isn't capability. Current models can already do useful work. The challenge is deployment: how do you give an AI agent enough access to be productive without creating security and quality risks? How do you measure whether it's actually helping? How do you maintain governance over outputs you didn't write?
We help engineering teams deploy agentic systems with clear boundaries, measurable outcomes, and governance built in from day one, whether you're rolling out coding agents to a 10-person team or designing autonomous workflows for enterprise operations.
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Read case studyWhat are agentic systems in software engineering?
Agentic systems are AI tools that can take autonomous actions, reading files, executing commands, making multi-step decisions. Rather than just generating text responses. In software engineering, this includes coding agents (Claude Code, Cursor Agent, GitHub Copilot) that can write code, run tests, review PRs, and modify codebases with varying levels of supervision.
How do coding agents differ from code completion tools?
Code completion tools (like basic Copilot) suggest the next line of code based on context. Coding agents can execute multi-step tasks: understand a bug report, navigate the codebase, write a fix across multiple files, generate tests, and submit a PR. More capability means more productivity potential, but also a wider risk surface that needs governance.
What is human-in-the-loop for AI agents?
Human-in-the-loop means requiring human review and approval at defined checkpoints in an agent's workflow, for coding agents, this typically means: the agent proposes changes, a human reviews them, and the human decides whether to accept, modify, or reject. The governance question is where to set those checkpoints based on risk tolerance.