Moduna

Decision intelligence for production AI agents

Know what users need. Fix what your agent misses.

Moduna turns production conversations into ranked intent, unresolved demand, and clear product and agent improvements—without manual transcript review.

Connect the stack you already run. Start with production evidence, not another manual review queue.

Example intent flow

Production conversations → roadmap action

Example data

Conversation signal

“I still haven't received my refund. Can someone actually help?”

Related conversations

1,284

Detected intent

Refund escalation

42% non-resolution

Repeated goal, rising frustration, unresolved outcome.

Ranked improvement

01

Add guided refund escalation workflow

Decision owner

Product + agent teams

From scattered conversations to a ranked product decision—without reading transcripts one by one.

Works with your agent stack

  • OpenTelemetry
  • LangChain
  • Vercel AI SDK

The problem

Your agent can look healthy while users stay stuck.

Traditional observability can confirm that a model responded and a tool ran. It cannot tell product teams whether the user achieved their goal—or which missing workflow is costing the business.

The signal is already in production.

What teams lack is a reliable intent layer that turns thousands of conversations into a decision.

AI agent team reviewing scattered conversation evidence and intent patterns
The signal exists across conversations, traces, and customer reviews. Without intent clustering, teams keep reading fragments one at a time.

Intent is buried

Users reveal their goals inside conversations, but those signals are scattered across thousands of chats.

Failures are silent

The agent may respond without throwing an error, while still failing to resolve the user's intent.

Roadmaps are guessed

Teams rely on manual reviews, loud customers, and anecdotal feedback instead of structured intent data.

The solution

Turn production conversations into a decision system.

Instrument the agent once. Moduna continuously groups real user goals, detects where resolution breaks, and ranks the product or agent improvements worth acting on.

Intent clusters
01
Failure patterns
02
Ranked actions
03
Python
from moduna import Instruments, Moduna

moduna = Moduna()
moduna.init(
    {
        "app_name": "customer support",
        "framework": Instruments.LANGCHAIN,
        "api_key": "mod_...",
    }
)

Opportunity radar

Find demand before it becomes a feature request.

See emerging needs, repeated workarounds, and high-value conversations that point to the next workflow, feature, or revenue opportunity.

Example emerging opportunity

Enterprise pricing handoff

+38%

Blind-spot detection

Catch failures that traces call successful.

Detect unresolved intents, clarification loops, missing handoffs, and confident answers that never actually help the user finish the job.

42%

Non-resolution

32%

Frustration rise

14

Blind spots

How it works

From production signal to the next decision.

  1. 01Connect

    Add Moduna to the agent stack you already run.

  2. 02Detect

    Cluster real conversations into goals, friction, and unmet demand.

  3. 03Improve

    Turn ranked blind spots into the workflow, prompt, or product change that matters next.

Shared production truth

One intent layer. Every team knows what to do next.

Product leaders and agent teams work from the same production evidence, reframed around the decision each group owns.

Executive team reviewing intent analytics on a conference room display

Executives

See whether your AI agent is solving the right problems.

  • Top unresolved intents
  • High-friction workflows
  • Missed automation opportunities
  • Business-impacting failure patterns
Product team planning roadmap priorities from user intent insights

Product Teams

Build from real user demand, not guesswork.

  • Emerging user needs
  • Recurring intent clusters
  • Feature opportunities
Agent team debugging production behavior with trace and intent data

Agent Teams

Improve agent behavior faster.

  • Failed handoffs
  • Clarification loops
  • Confusing responses

Product evidence

The intent dashboard for AI agent teams.

Moduna shows what users want, where the agent breaks, and what to improve next.

12.4k
Conversations analyzed
38
Intent clusters detected
27%
Average non-resolution rate
14
Agent improvement opportunities found
Executive summary
Top opportunity
This week's top intent opportunity: Refund escalation

Why it matters: Refund-related conversations account for 18% of unresolved demand. Frustration is up 32% week over week, and 42% of these conversations fail to reach resolution.

Recommended agent improvements
Ranked opportunities for the next roadmap review
1

Add guided refund escalation workflow

2

Improve billing dispute handling

3

Route enterprise pricing questions to sales earlier

Intent clusters
Ranked by unresolved demand and likely agent impact
IntentVolumeFailure RateImpactRecommended Agent Improvement
Refund escalation1,28442%HighAdd guided refund workflow
Billing dispute91236%HighImprove resolution policy
Subscription cancellation74029%MediumAdd retention flow
Enterprise pricing41825%HighRoute to sales earlier
Password recovery39018%MediumImprove authentication handoff
Intent trends
Loading dashboard chart view

Start with production truth

Build the next improvement from what users actually need.

Connect Moduna to the agent you already run. Turn every production conversation into evidence for a better product and a more capable agent.