Every company wants to “do something with AI”. Most already have ideas where AI could help.
The challenge isn’t ambition. It’s moving from an idea to something measurable, without spending six months and significant budget only to discover it wasn’t the right call.
Most AI initiatives don’t fail because the technology doesn’t work.
They fail because:
- scoping takes too long
- pilots lack rigour
- and the path to production is unclear.
Wrong use case.
Unclear ROI.
Poor data quality.
No operational ownership.
According to Gartner, 40% of agentic AI projects will be cancelled by the end of 2027 due to unclear value, cost overruns, and inadequate risk controls.
That’s why we created the Agentic Sprint. A fast, focused way to validate whether AI can solve a real business problem, before committing to a full build.
From AI Idea to Working Prototype in 10 Days
The Agentic Sprint turns AI ambition into a working prototype in just 10 working days.
Fixed scope.
Fixed fee.
No lock-in.
No endless workshops.
No vague experimentation.
No drawn-out discovery phases.
Just one focused use case, tested against real business conditions.
By Day 10, you leave with:
- a working agent prototype on your data
- KPIs measured from day one
- a costed path to production
- and a clear go / no-go decision
As we like to say: You leave with a decision, not a maybe.
How the Sprint Works
Days 1–2 → Frame
We identify the right use case, define measurable KPIs, validate data availability, and confirm technical feasibility.
We don’t build until the case and the data are real.
Days 3–8 → Build
We design and deploy a working AI agent prototype using your workflows, data, and environment.
Not a scripted demo, not a proof-of-concept built on clean sample data.
A working prototype you can actually pressure-test.
Depending on the use case, the sprint can include:
- workflow automation
- document analysis
- knowledge agents
- decision-support tooling
- multi-agent collaboration
- and human-in-the-loop approval mechanisms
The goal is simple: prove whether the workflow creates real operational value before scaling it further.
Days 9–10 → Prove
We measure the prototype against the KPIs defined at the start of the sprint.
You receive:
- measurable results
- a production roadmap
- and a clear recommendation for next steps
Whether the answer is “scale it” or “stop here”, you gain clarity fast without months of uncertainty.
What This Looks Like in Practice
One recent sprint involved an enterprise product and engineering organisation struggling with slow product discovery workflows.
And the bottleneck wasn’t engineering.
Senior product managers were losing time chasing context across Jira, Confluence, and Teams threads before features could even enter development.
Each feature request took an average of 11 days to move from raw request to approved specification.
Q built a Knowledge & BA Agent integrated directly into the client’s existing tooling.
The result:
- raw feature requests automatically converted into structured specs
- acceptance criteria and edge cases generated automatically
- human review built into every approval step
- and discovery cycles reduced from 11 days to just 4
Across more than 250 backlog items, the client identified the potential to recover approximately 1,750 senior PM days annually. Not through replacing teams. Through removing operational friction.
Case Study: Multi-Agent Due Diligence for a NYC Private Equity Client
One of our recent Agentic Sprint engagements involved a New York City private equity client operating under aggressive M&A timelines.
Their legal and financial due diligence process was heavily manual, time-sensitive, and difficult to scale.
Analysts were spending days reviewing large volumes of documentation under tight investment windows, creating operational bottlenecks and increasing the risk of missing critical findings.
Q designed and implemented a custom multi-agent AI system capable of:
- document ingestion
- legal analysis
- financial analysis
- automated risk flagging
- and human-in-the-loop validation
The result:
- due diligence cycles reduced from days to just 4 hours
- broader document coverage
- faster analyst throughput
- and lower operational risk
Most importantly, the system didn’t replace analysts.
It amplified them.
Instead of spending time buried in repetitive document review, teams could focus on strategic judgement and faster deal execution.
Why Companies Choose Agentic Sprint
The most valuable AI systems today aren’t standalone chatbots. They’re embedded into workflows, connected to operational systems, and designed to solve specific business bottlenecks.
Companies use Agentic Sprint to:
- validate AI use cases fast
- reduce investment risk
- identify real operational ROI
- automate manual workflows
- accelerate internal buy-in
- and move from experimentation to execution
The goal isn’t AI for the sake of AI. It’s measurable business impact.
Why Q
A two-week sprint is easy to copy. What stands behind it is not.
At Q, AI agents are already embedded into our own delivery workflows across engineering, QA, analysis, and operations.
Every engagement is backed by:
- production-grade engineering teams
- private AI infrastructure
- human-in-the-loop governance
- and a clear path from prototype to deployment
Your data stays yours. No client data ever touches public AI tools.
The Fastest Way to Understand AI Value Is to Test It
Eventually, every organisation reaches the same point: they need to see AI work inside their own business. That’s exactly what the Agentic Sprint is designed for.
One use case in.
A working agent out.
And a clear path to production.
Have an AI Use Case You Want to Pressure-Test?
If your organisation has:
- repetitive workflows
- document-heavy operations
- manual analysis bottlenecks
- or processes slowed down by fragmented systems
we’d love to explore it with you.
Bring us one workflow. We’ll show you what an AI agent could do with it.
Get in touch with the Q team to book an Agentic Sprint discovery session.
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ABOUT AUTHOR
Kostana Pavic
Event & Communications Specialist
