Use case
AI Agent & Chatbot Feedback
Measure whether your AI agent actually helped. Capture a rating and the reason after every conversation through an API built for software.
AI agent feedback is how you measure whether your chatbot or LLM assistant actually helped. By capturing a rating after each conversation — and the reason behind it — you get the ground-truth signal that evaluation benchmarks miss: did this real user get what they needed?
Why AI agents need a feedback loop
LLM apps are easy to ship and hard to trust. Offline evals tell you how a model does on test cases, but only real users can tell you whether your agent is helpful, accurate, and worth keeping. A feedback prompt after each response turns every conversation into labeled data you can improve on.
- Per-turn or per-conversation ratings — thumbs, stars, or CSAT on the answers that matter.
- The reason behind the score — capture the open-ended "why" that explains a bad rating.
- Rich context — attach the model, prompt version, tools used, or session ID to every submission.
Built for AI agents, not bolted on
Pollenate is designed to be called by software, not just embedded in a page. Your agent can submit feedback directly through the collect API, and Pollenate ships an AI skill file, an OpenAPI spec, and a Model Context Protocol (MCP) server so agent frameworks can discover and use it natively. Capturing a rating is a single authenticated API call.
curl -X POST https://api.pollenate.dev/collect \
-H "X-Pollenate-Key: $POLLENATE_KEY" \
-H "Content-Type: application/json" \
-d '{"inboxKey":"...","type":"thumbs","score":1,"meta":{"model":"gpt-4o","sessionId":"abc"}}'
Find out where your agent breaks down
Once ratings are flowing, AI semantic search lets you ask your feedback questions in plain language — "where did users say the answer was wrong?" — and clusters the conversations that need attention. You see which intents, prompts, or model versions underperform, and you prioritize fixes with evidence.
Improve with every conversation
Tie feedback to model and prompt versions and you can measure the impact of each change: did the new system prompt actually raise satisfaction? Pollenate's real-time dashboard and automations mean a spike in thumbs-down can alert your team instantly, before it becomes a trend.
Why AI teams choose Pollenate
- A collect API, MCP server, and skill file built for agents
- Attach model, prompt, and session metadata to every rating
- AI semantic search to cluster failure modes
- Real-time dashboards and instant alerts
- Free to start, with developer-first APIs and SDKs