Loop

Agent Attribution

Capture every agent and seller action, then trace it back to the revenue they create.

THE PROBLEM

Do you know when your agents help close deals?

Your agents log activity: triggers, tool calls, and tokens spent. But it's all just an activity metric, while your CRO's asking about the revenue outcomes.

  • Agent activity · no revenue link Activity only
  • Claude → 14 tool calls · 8.2K tokens
  • Copilot → 7 triggers · 3.1K tokens
  • Custom → 22 actions · 12K tokens

3 agents active · 0 deals attributed

Observability watches the agent. It never watches the revenue.

WHY IT HAPPENS

LLM observability tells you latency, error rates, and token spend. But it never joins an action to a deal, an account, or a closed-won dollar.

HOW IT WORKS

Agent Attribution in three steps.

01

Log every agent and seller action.

Loop captures tool calls, triggers, and actions in real time alongside the rep activity on the same deal.
02

Match each action to its revenue outcomes.

Actions are joined to the deal, account, and stage with context, so metrics like conversion rate, ACV and cycle times are all attached to the work that drove it.
03

Report impact in revenue.

Run the analysis to isolate impact across deal variables, creating your "attributed revenue" metrics with the full trace you can audit and defend.
Customer Outcomes

Teams that ship attribution see compounding results.

100%
Actions traced to revenue
3.1x
Agent-assisted close rate
<1hr
Time to first attribution
"Fluint gave us months of AI engineering work with a single integration. Now everything we build is grounded in how we win, with the right context for every chat, skill, agent and seller."
Quincy L, GTM AI Lead
Defensestorm
HOW IT COMPARES

Fluint vs. AI observability tools. Why telemetry isn't enough.

Observability watches the agent. Fluint watches the revenue.
What matters
Fluint
Recommended
Context graphs * search
e.g. glean, notion ai
Revenue judgement
Deal dimensions with revenue impact isolated & labeled
Map connections
Improves over time
Yes, closed loop retrains on the outcome
Re-curated when relationships change
Carries process & rules
Yes, into every workflow
Varies, mostly entities
Delivery & upkeep
One MCP call, fully managed
MCP, with DIY inputs to maintain
Model-agnostic
Yes, outlives any one model
Usually
What matters
Fluint Recommended
AI observability
Datadog, Arize, LangSmith
Joins to CRM outcomes
Yes, every action mapped to the deal
No, telemetry only
Answers 'did it close?'
Yes, attributed to closed-won
No, stops at 'it ran'
Agent vs. rep credit
Split across both on the deal
Not modeled
Revenue, cycle, ACV impact
Per play, with trace
Not available
Built for
Revenue teams
Engineering and SRE

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Connect your CRM and one call source and get your first attributed outcome the same session.