Agent Analytics
What’s the real impact of your AI agent?
Observe what users ask your AI agent, evaluate where it fails and what those failures cost your business. Agent Analytics ties traces and evals to conversion, retention, and revenue, so you can finally connect agent quality to business impact.

Sessions
Which users only engaged with our agent once?
What are my agents' most common failure modes?
What questions are being answered badly?
How and why are users using the create_theme tool?
One financing quote timed out, but inventory and trim data came back clean. Three cars fit your budget and safety bar:
- Best match: 2024 Kia Telluride EX — $43,190, IIHS Top Safety Pick+
- Runner-up: 2024 Honda Pilot EX-L — $44,700, seats 8
- Watch out: the Highlander XLE clears $46,800 once you add all-wheel drive
Two Tellurides are in stock at Northgate Kia. Want me to book a test drive this weekend?
Summary
Matched a $45k 3-row SUV request to three models and booked a Saturday test drive for the Telluride.
Usage
Configuration
Signals
Topic

Building AI features creates new blind spots
Two users can ask an agent the same question and get completely different outputs. AI can hallucinate, ignore instructions and confidently give incorrect answers. Additionally, both those users who spent time with your agent look equally engaged on a traditional analytics dashboard. Who actually found it to be useful?
Go beyond observability
Learn the product and revenue impact of your agents.
AI Quality
What the agent did
Product Outcomes
What the user did next
01 Observe
Inspect traces, prompts, tool calls, responses, latency, and cost. Limitless segmentation by semantics and behaviors.
02 Evaluate
Score quality and failure modes. See where the agent helps and where it adds risk.
03 Decide
Tie those quality signals to conversion, retention, and revenue. Did the agent actually move the user forward?
04 Deploy
Run experiments, deploy personalized guides and optimize agent harness.

Trace turns automatically become Amplitude events
All agent interactions including a user message and AI response are agent behaviors that you can query on a chart alongside your typical behaviors (session start, page viewed, button clicked). Unlike observability tools that stop at the trace, Agent Analytics decomposes these conversations into events, making them directly queryable in the same funnels, cohorts, and retention analyses you already use.
The questions you can finally answer
01
Which model drove higher sign-ups or workflow completions?
02
Does conversion drop when AI agent hallucinates vs. answers correctly?
03
What paths do users take before and after engaging our AI agent?
The Agent Analytics maturity model
Most observability tools stop at the lower levels of maturity. Agent Analytics takes you to the top by connecting AI quality to the user journey and revenue.
What's the ROI of this agent for my business?
How does AI usage affect the user journey?
Are users happy or frustrated?
Did the agent do it correctly?
Can I see what happened?
Inside Agent Analytics






Production runs surprise teams with questions they never prepared the model for. Read the user prompt, the agent’s response, the tools it called, and the context it pulled, including seeing Session Replays, to see what went wrong.
One financing quote timed out, but inventory and trim data came back clean. Three cars fit your budget and safety bar:
- Best match: 2024 Kia Telluride EX — $43,190, IIHS Top Safety Pick+
- Runner-up: 2024 Honda Pilot EX-L — $44,700, seats 8
- Watch out: the Highlander XLE clears $46,800 once you add all-wheel drive
Two Tellurides are in stock at Northgate Kia. Want me to book a test drive this weekend?
Evaluators
BETADetect when responses cite unsupported facts
Did the agent successfully complete the user's task?
Rate the quality of the agent's tool selection
Cluster reasons users abandon the checkout flow
Description
Grades every matching session for Tool Use Quality and writes the verdict back to the session as a property — available in charts, segments, and exports.
Grading prompt
Edit5 — fully correct, efficient, and well-justified
3 — partially correct or with minor issues
1 — incorrect, unsafe, or off-task
Weigh tool selection and reasoning, not just the final answer.
Label distribution · last 30 days
Configuration
Monitor
Last 30 daysChat AgentSearch…Agent Adoption
Active users for Chat Agent
Agent Activity
Conversation volume for Chat Agent
Failure Rate
Errored sessions for Chat Agent
Daily Spend
Inference cost for Chat Agent
Done. Here’s the median weekly check-ins per location over the last 30 days:
Downtown Flagship and Riverside stand out — they drive 2–3x the check-ins of every other location. Worth a closer look at staffing and class capacity there.
View chart →Response accuracy
Member friction
Resolved the request
Member intent topic
Overall quality
Comments
Customer Stories
Hear directly from customer teams using Agent Analytics.
Instrument any LLM provider
Native wrappers for the providers you already use.
Python and Node, drop-in SDK, live in minutes.
Content-optional analytics
Purpose-built to let you control what leaves your environment.
Send full conversations, your own labels, or metadata only. Switch modes per agent or per event source.
Agent Analytics is available to all customers
Stop shipping on vibes
- Observe traces and score agent quality.
- Learn the conversion patterns and failure modes that cost you.
Frequently asked questions
The analytics layer between LLM observability and product analytics. Every user message, tool call, agent response, and session end becomes an Amplitude event tagged with topic, quality score, and behavioral pattern, so you can build cohorts, funnels, and retention curves on AI session quality.
A new era of analytics
From a live agent overview to evals and datasets, every view ties what the agent did to what the user did next.












