Agentic UX & Copilot Blueprint
Seed to Series B · $8,999 · 2 Weeks · Strategy + UX Sprint
Users don’t know when to trust the agent, what it can do, or how to recover when it goes wrong. So they stop using it. Or worse — they use it, it does something they didn’t ask for, and they lose trust in the whole product.
This sprint architects the full agentic layer before a single UI decision is made — where automation belongs, where it doesn’t, and the UX patterns that make every agent action feel earned rather than arbitrary.
This is for you if
- You’re adding agents, copilots, or autonomous workflows to an existing product and don’t have a clear UX model for where automation begins and ends
- Your early agent features generated confusion, support tickets, or churn — and you’re not sure if the problem is the model or the UX
- You’ve been rebuilding agent UI sprint-by-sprint with no coherent framework underneath it
What you get
| Deliverable | Why it matters |
|---|---|
| Agent opportunity map | Where agents should and should not exist in your product — with the rationale behind every boundary, not just a diagram |
| Decision model | Structured framework defining what’s fully automated, what’s suggested, and what requires explicit user confirmation — built before UI design starts |
| Prompt + context blueprints | Input/output design, context window scoping, and data boundary documentation your engineering team actually builds against |
| 3–5 high-fidelity agentic flows | Task delegation, error recovery, confirmation loops, progress visibility, and override patterns — the moments that make or break agent adoption |
| Dev-ready specs | Component annotations and interaction notes formatted for immediate handoff |
| Agentic UX principles document | A reusable framework your team applies to every future agent feature without rebuilding the logic from scratch |
One sprint. One coherent architecture. No overlapping outputs, no “we’ll figure that out next sprint” scope gaps.
Architecture-first vs. bolting it on
| Traditional Approach | Poplab Blueprint |
|---|---|
| UI team adds “AI mode” to an existing feature | Agentic behavior mapped before any UI is built |
| 3+ months iterating to find the right automation scope | Decision model defines automation boundaries upfront |
| Users confused by what the agent does — support spikes | Every agent action is legible, reversible, or confirmable by design |
| UX rebuilt every time a new agent feature ships | Reusable agentic framework your team extends independently |
| 3+ months · fragmented decisions · trust erosion | 2 weeks · one sprint · a system that scales |
Every sprint you invest in agent UI without a decision model is sprint debt. You’ll pay it back in confused users, escalated support, and churn from the people who needed that feature most.
How it works
Week 1 — Architecture & Decision Mapping (Days 1–7)
Kickoff + AI capability audit + competitive agentic UX teardown. Decision model workshop to define the full automation spectrum. Agent opportunity map + context blueprint drafts. End-of-week review to align on scope before design begins.
Week 2 — UX Design & Dev Handoff (Days 8–14)
3–5 high-fidelity Figma flows for key agentic moments. Synthetic user testing through each flow. Dev-ready specs + agentic UX principles document. Loom walkthrough + implementation Q&A call.
Proof
- ~30% increase in VDR feature adoption within the first week (AI VentureFactory) — after agentic feature entry points were redesigned with clear boundaries, legible actions, and trust-building transparency patterns
- 2.4x task discoverability + ~50% fewer navigation errors (AI VentureFactory) — the direct result of mapping where automation belongs in the product before designing the UI around it
- ~25% reduction in support-driven UX queries (AI VentureFactory) — because error states, override patterns, and “why did the AI do that?” moments were designed in from the architecture layer
- 38% reduction in support tickets + 91% task success on critical workflows (Mimecast) — after a structured UX framework replaced ad-hoc feature-by-feature decision-making across the product
Agent confusion isn’t a model problem. It’s an architecture problem. Fix the architecture once; every future agent feature inherits the solution.
Pricing & Terms
- Price: $8,999 (excl. VAT)
- Timeline: 2 weeks from kickoff to final handoff
- Payment: 50% to reserve the slot, 50% on delivery
- Availability: 2 Blueprint slots per month
Build an agentic experience users will actually trust
Book a 20-minute call. We’ll map where your current agent UX is breaking trust, identify the decision model gaps, and tell you whether this sprint is the right lever to turn your AI capability into a product advantage — not a support burden.