From ticket to shipped code
ticket → coding agent → AI review → human approval → shipped
time to first change · number of fixes · review time
Service · 01
Agentic Workflow Sprint
In 2–4 weeks we take one real process and test whether AI can turn it into a measurable workflow — with an owner, data, Draft/Approve/Auto modes and before/after metrics.
Who it’s for: For teams that already have an AI demo, or suspect a specific process is ripe for automation.
( 01 )
A demo is not an implementation.
A demo looks great on stage, but has no owner, metrics, data, integrations or risk control. The sprint turns it into a workflow that has all of that — or ends with an honest decision that it’s not worth it.
( 02 )
What you get
- Workflow spec: what the human does, what AI does, where approval sits
- A working prototype on real data
- Draft / Approve / Auto modes with autonomy boundaries
- Before/after metrics: time, cost, quality, scale
- Guardrails and control rules
- Decision: scale, improve or kill
( 03 )
How it runs
01Week 1Context and selection: process, owner, data, definition of a good result.
02Week 2Design and build: workflow spec and the first working version.
03Week 3Test on real cases: metrics, fixes, autonomy boundaries.
04Week 4Decision: before/after results and a recommendation — scale or kill.
( 04 )
What you need on your side
The sprint works if you have:
a single process owner
a process to test
data or examples of real cases
agreement to measure the effect
access to tools, or a team that can connect them
readiness for a post-sprint decision: scale or kill
( 05 )
Three example sprints
Support & inbox triage
classification → priority → drafted reply → approval → send or escalate
first response time · cases handled · escalation rate
Natural-language search
plain-language query → interpretation → mapping to data → results & recommendations
usage · conversion · returns
( 06 )
What’s next
Handoff to your team, a scale plan, another sprint or ongoing advisory — depending on the result and the decision.