Firstflow: The Experience Layer for AI Agent Products
Firstflow Team
Agents are live. Onboarding isn't.
Previously, we were building conversational AI products and running into the same wall every time. The model was good, the infra was solid — but users weren't activating. After talking to teams shipping agent products for months, we realized the agent wasn't the problem. It was the experience layer around it.
The problems with agent onboarding today
- Static onboarding doesn't fit conversational products. Modals, tooltips, and dashboard checklists were designed for SaaS apps, not agents. They break the flow and feel out of place.
- There's no native way to guide users inside a conversation. Teams are hacking together prompts, hardcoded messages, and one-off flows — none of which scale or measure well.
- Discovery is broken. Users don't know what the agent can do. Features go unused, not because they're not useful, but because no one ever told the user they existed.
- Feedback and input collection is an afterthought. Getting structured information from users mid-conversation — surveys, preferences, confirmations — means building it from scratch every time.
- Response quality is invisible. When an agent gives a bad answer, users just leave. There's no native way to capture a thumbs down, understand why, or route that signal anywhere useful.
- Issues have nowhere to go. Users who hit a problem mid-conversation have no way to report it in context. Teams find out through support tickets days later, if at all.
- Nothing is measurable. There's no visibility into where users drop off, what they engage with, or how onboarding flows are performing.
What Firstflow is
The experience layer for AI agent products. Firstflow is a platform that lets teams design structured in-chat experiences — capability discovery, skill unlocks, surveys, announcements, feedback flows, and step-by-step input collection — that feel native to the conversation. Here are the highlights:
- In-chat by design — Every flow lives inside the conversation, not on top of it. No popups, no redirects.
- Guide users to value — Introduce features progressively, at the right moment, so users discover what your agent can do as they're ready for it.
- Collect structured input — Run surveys, onboarding questionnaires, or preference collection as natural conversation steps.
- Personalize as you go — Flows adapt to each user based on what they've done, said, or unlocked — making every experience feel tailored.
- Response feedback, built in — Let users rate any agent response with a like or dislike, optionally capturing the reason why — so you know what's landing and what isn't.
- In-chat issue reporting — Users can flag problems right inside the conversation, with full context attached. No support ticket, no lost signal.
- Measure what matters — Track engagement, completion rates, drop-off, and feedback trends across every flow so you know what's working.
Example Use Cases for Firstflow
You're launching a customer-facing AI assistant and need users to understand its top three capabilities in the first session — without dumping an instruction manual on them.
You want your sales agent to collect qualification information from a new prospect naturally across the first few messages, before handing off to a human rep with full context.
You're rolling out a new feature to your agent and need to announce it to existing users, collect initial feedback, and track who engaged — all within the conversation itself.
You need to progressively unlock advanced capabilities for users as they hit milestones, turning your agent into a product that grows with them.
Your support agent handles hundreds of conversations a day and you want users to be able to flag a bad response or report a broken flow without leaving the chat — and have that report land in your team's queue with full conversation context attached.
You're iterating on your agent's responses and need a real signal on what users find helpful versus frustrating. With per-message feedback and optional reason capture, you get the qualitative data to actually improve it.
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