Everyone at Ably should be using AI to see how they can make themselves more effective. But it's not just about doing things faster. It's about doing things you couldn't do before. The goal is to shift the mindset, where people stop asking 'can AI help with this?' and start assuming it can, then push further: what's now possible that wasn't?"
"The Ably MCP connects all our internal tools together," Jamie explains. "It lets people access data across systems via AI assistants. Building this and seeing it genuinely change how people work has been incredibly rewarding."
What started as an experiment to see what was possible has grown into a company-wide platform that's now critical to daily workflows, integrating 15+ services through over 140 tools. Engineers can check CI build status and debug workflow failures without leaving their conversation. Product managers search across Jira issues, GitHub PRs, and Slack threads in a single query. Sales teams pull Gong call transcripts and HubSpot contact history to prepare for customer meetings. The breadth is significant: GitHub, Jira, Confluence, Slack, HubSpot, Gong, Jellyfish, Metabase, PagerDuty, GSuite and more, all accessible through natural conversation.
Before MCP, every AI interaction started from zero, engineers manually explaining Ably's infrastructure, marketers pasting in brand guidelines, constant context-switching that made AI feel like more work rather than less.
Now when an Ably employee opens Claude, they're not starting from scratch. Through MCP, they have immediate access to:
• Shared context and prompt library • Company knowledge and documentation • Ably's tone of voice guidelines and style guides • Live data from internal tools and systems
Scaling to 140+ tools created its own challenge: context limits. Ably solved this with a tool registry that lets the AI discover only what it needs for each task, keeping interactions lean and responsive.
One principle remains constant: a single human author owns every PR, regardless of how much was AI-generated. The practice of engineering judgment, knowing what to accept, what to push back on, and what to rewrite, is still the job.
We run AI drop-in sessions every Friday where team members can bring questions, share what they've built, or explore new ideas. An internal Slack channel serves as a continuous stream of AI experiments, wins, and collaborative problem-solving.
"When Charlotte [Delivery Manager] and I approach teams, we don't even talk about AI initially," Jamie reveals. "We ask: what are your repetitive processes? Once teams understand their processes, then you can start the AI conversation."