How Five Points Cut Manual Reporting Time by 80% with AI Agents

Eighty percent of the time Five Points’ senior engineers spent on status updates and cross-team handoffs is now handled by an AI agent. They didn’t overhaul their process. They didn’t retrain their team. They added one layer — and the overhead disappeared.

This is how they got there.

The Client

Five Points is a software consultancy managing the TFI One project — a complex multi-team initiative with active development, ongoing stakeholder reporting, and the kind of coordination overhead that tends to eat senior engineers alive.

Their team was technical, disciplined, and good at the work. The problem wasn’t talent — it was all the work that surrounds the work.

The Challenge

When a project runs across multiple teams and multiple tools, coordination becomes a job in itself.

Issues created in one system needed manual status updates in another. Pull requests sat in a queue because the engineer who needed to review them was in a meeting. Progress reports required someone to aggregate state from five different places — every sprint, every week, every standup.

The work wasn’t hard. But it was constant, it was interruptive, and it was expensive.

Senior engineers — people hired to solve hard technical problems — were spending several hours each week on work that required judgment only to determine who to notify and what format the update should be in. The rest was mechanical.

The other cost was less visible: context switching. Every time an engineer stopped to file a status update or triage an incoming issue, they paid a reentry cost to get back to the problem they were actually solving. Over a sprint, those interruptions compounded into real velocity loss.

The Approach

Five Points integrated CLAIRE into their TFI One development pipeline as a managed agent subscription.

The deployment was phased. In the first two weeks, CLAIRE took ownership of issue triage — reading each new item as it arrived, assigning priority, tagging it appropriately, and routing it to the right team. Engineers stopped seeing a raw inbox. They saw a curated queue.

In the second phase, CLAIRE absorbed the status reporting layer. At the end of each sprint cycle, it generated structured progress reports from the actual state of the issue tracker and codebase — no one had to hand-curate the summary. When stakeholders asked for updates mid-sprint, the answer was a query, not a meeting.

The third phase was cross-team handoffs. When a piece of work crossed a boundary — development to QA, backend to frontend, engineering to product — CLAIRE handled the transition: posting the handoff context, flagging blockers, and confirming receipt. The handoffs that used to disappear into Slack now had a clear owner and a clear record.

The integration with their existing Azure DevOps and GitHub setup was handled by CLAIRE’s plugin layer. No new tooling. No workflow migrations. CLAIRE connected to the systems Five Points already ran.

The Result

Eight weeks after deployment, Five Points measured the outcome.

Manual reporting time had dropped by 80%. The hours previously spent aggregating status, filing updates, and chasing down handoff confirmations were gone — handled automatically, with better consistency than the manual process it replaced.

Engineers reported fewer interruptions per day. Sprint velocity improved. The senior engineers who had been carrying the coordination overhead got that time back and redirected it to the technical problems the project actually needed them on.

The reporting layer that used to require a dedicated sync now runs in the background.

What Made It Work

Two things stood out in the Five Points deployment.

First, the work was specifiable. Issue triage, status reporting, handoff management — each of these has a clear definition of done. CLAIRE works best on tasks where the output can be described in advance. The Five Points team had already done that work; they had process documentation, they had standards. CLAIRE was the execution layer, not the design layer.

Second, the team didn’t try to do everything at once. The phased approach meant they could calibrate CLAIRE’s output against their expectations at each stage, adjust where needed, and expand from a position of confidence rather than guesswork.

The result was a deployment that felt natural rather than disruptive — because it was built on work the team already understood.

Want similar results for your team?

If your engineers are spending hours each week on coordination work that should be automatic, that’s the right place to start. Book a call — we’ll identify which workflows make sense to automate first and show you what the output looks like before you commit to anything.