What AgentIQ Does
Enterprises are deploying AI agents.
Nobody knows how they're being used
or whether they're working.
AgentIQ connects to your agents and gives you two things no current tool provides.
Usage analytics — what users are actually asking for, which workflows run most,
where sessions drop off, and how quality varies by intent.
Loss pattern analysis — automatically surfacing where agents fail,
why they fail, and giving developers the structured evaluation data
to fix them. Causal inference then proves whether improvements
created real business impact.
$11.6B
Spent on enterprise AI agents in 2026
71%
Of executives cannot prove their AI is working
40%
Of AI agent projects fail from lack of oversight
21%
Of enterprises have any mature agent oversight model
// Real Incident · March 2025
A fintech company deployed an AI agent for transaction reconciliation.
It looped for 11 days. $47,000 in costs. Nobody noticed.
Their tools showed the agent was running.
None of them showed what users were asking for, where it was failing, or why.
There are two problems enterprises face with agents today — and most tools solve neither.
Nobody knows how agents are actually being used
What are users asking for most? Which workflows run? Where do sessions drop off? Which intents perform worst? These are basic questions every team has and nobody can answer today. There is no usage analytics layer for agents.
Nobody measures agent response accuracy
Metric frameworks surface agent performance in a shallow way — task completion, latency, token counts. None measure whether agent responses were actually correct, relevant, or aligned with the intended goal.
Loss pattern analysis doesn't exist
Agents fail in patterns — the same intent, the same workflow step, the same tool call three levels deep. Nobody surfaces these patterns automatically. Teams discover failures by accident, not by design.
LLM evals don't run autonomously on agents
We are still in the era of LLM evals — manual, developer-triggered, point-in-time. Agent evals are fundamentally different because agents are multi-step workflows. Failure can happen at any step in the chain. No platform evaluates this continuously and automatically.
Business value of agents is never measured
Consumer-facing features sometimes get measured through controlled experiments. Most agents never do. There is no systematic connection between agent quality and business outcomes. Teams run agents indefinitely with no proof they are creating value — or destroying it.
Two layers. One platform.
Layer 1: understand how your agent is being used.
Layer 2: understand where and why it is failing within that usage.
1
Connect — live in minutes
AgentIQ integrates via API with any agent framework —
LangChain, CrewAI, or custom builds. No rebuilding required.
Plug in and immediately start measuring.
2
Understand usage — intents, paths, drop-off, tool behavior
AgentIQ classifies every session by user intent, maps the most common workflow paths,
shows where sessions drop off, and surfaces tool failure rates.
Quality is broken down by intent — showing exactly which user needs
the agent handles well and which it consistently fails.
This is the foundation every other insight builds on.
3
Measure accuracy — LLM judges on every interaction
LLM-as-a-Judge evaluation runs autonomously on every agent interaction —
scoring response accuracy, goal alignment, and decision quality
continuously, without developer intervention.
Every interaction is scored. Nothing is sampled.
4
Diagnose — automated loss pattern analysis
AgentIQ automatically identifies where agents fail across the workflow —
which intents, which steps, which tool calls, at which point in the workflow.
Not "your agent has a 23% failure rate" —
but "billing disputes account for 47% of all your failures
because the payment API call times out at workflow step 3."
5
Fix — structured eval data for developer RL loops
AgentIQ delivers scored interactions, labeled failure patterns,
and root cause signals to your developers.
Your team owns the domain knowledge of what good looks like.
AgentIQ gives them the structured data to run
their own reinforcement learning improvement loop.
6
Prove — causal inference on every improvement
After developers deploy an RL improvement,
AgentIQ applies causal inference to isolate true business impact from noise.
Not "eval score improved by 8%" —
but "this improvement causally drove
a 12% increase in successful resolutions.
Here is the confidence interval."
Context.ai proved the market — OpenAI acquired them in April 2025
because evaluation was too critical to leave to a startup.
AgentIQ is what they would have built next.
Context.ai · Acquired by OpenAI
Visibility only
Showed how AI models were being used
Grouped conversations to surface usage patterns
Analytics dashboard for model performance
No intent classification or workflow path analysis
No drop-off analysis or quality by intent
No autonomous LLM eval on every interaction
No automated loss pattern analysis
No structured eval data for developer RL loops
No causal proof of improvement
AgentIQ · 2026
Usage analytics + accuracy + diagnosis + data + proof
Intent classification — what users actually ask for
Workflow path analysis — most common sequences
Drop-off analysis — where sessions abandon
Quality by intent — which intents fail most
Autonomous LLM judges on every agent interaction
Automated loss pattern analysis with root cause
Structured eval data for developer RL improvement loops
Causal inference — proves business impact, not just eval scores
Existing tools were built for engineers —
traces, latency, token counts.
None show usage patterns by intent. None surface loss patterns automatically.
None close the loop from RL improvement to causal business proof.
Company
What They Do
Usage Analytics
Loss Patterns + Causal
LangSmith
LangChain
Traces and debugs agent workflows. Built for engineers, stops at the engineering layer.
None
None
Arize
$68M raised
ML model monitoring and drift detection. Strong observability, no loss patterns or causal proof.
Partial
None
Galileo
$68M raised
GenAI evaluation dashboards. Good visibility, no automated loss patterns or causal proof.
Partial
None
Patronus AI
$20M raised
LLM-as-a-Judge and hallucination detection. Strong on output eval, no workflow loss patterns.
Partial
None
Context.ai
Acquired · OpenAI
Analytics for how AI models are used. Closest to our vision — acquired by OpenAI April 2025.
Acquired
Acquired
AgentIQ ✦
Seed · 2026
Usage analytics (intents, paths, drop-off, quality by intent) + autonomous LLM eval + loss pattern analysis + structured RL data + causal proof of business impact.
Core
Core
Methodology built at Google scale
The founder spent 7 years at Google building LLM evaluation systems,
loss pattern frameworks, and causal inference infrastructure
for production AI. This methodology — rigorous agent quality measurement
and causal business proof — is encoded into AgentIQ's core.
Nobody else starts here.
Gets sharper with every deployment
Every deployment generates evaluation signal
that sharpens AgentIQ's LLM judges
and enriches its loss pattern library across agent types and industries.
More customers means more accurate evaluation
and a compounding gap against every alternative.
Becomes the system of record
AgentIQ becomes the source of truth for agent quality —
feeding RL loops, board reporting, and compliance decisions.
Replacing it means losing the full history
of every loss pattern, improvement cycle, and causal proof.
Nobody does that.
The agentic AI market grows from $7B in 2025 to $93B by 2032 at 44.6% CAGR.
The evaluation and governance layer historically captures 10–15% of total platform spend.
Every new agent deployment is a new measurement problem — and a new customer need for AgentIQ.
Market · 2025
$7B
Agentic AI total market today
Market · 2032
$93B
44.6% CAGR — fastest growing enterprise tech
Our Layer · 2032
$9–14B
Eval & governance at 10–15% of market
Beachhead
Mid-Mkt
500–5K employee enterprises deploying agents now
Standard · 30 Seconds
Enterprises are deploying AI agents at scale —
and almost nobody knows how those agents are being used
or whether they are actually working.
There is no usage analytics layer for agents.
No automated evals. No loss pattern analysis.
No connection between agent quality and business outcomes.
AgentIQ fixes all of this.
Usage analytics show what users are asking for,
which workflows run most, where sessions drop off,
and which intents have the worst quality.
Autonomous LLM judges evaluate every agent interaction continuously.
Automated loss pattern analysis surfaces exactly where agents fail and why —
down to the specific intent, workflow step, and tool call.
Developers get structured evaluation data to run their own RL loop.
Then causal inference
proves whether the improvement moved the business needle.
Context.ai built the usage visibility layer.
OpenAI acquired them in April 2025.
AgentIQ is what they would have built next —
usage analytics plus automated failure analysis plus causal proof.
Their customers need a replacement now.
Bear Pitch · For the Hard Room
"I know what you're thinking.
Galileo raised $68M. Arize raised $68M.
The eval space looks well-funded.
Here is the gap none of them fill:
agent evals are fundamentally different from LLM evals.
Agents are multi-step workflows.
Failure can happen anywhere in the chain.
None of these platforms run autonomous evaluation on agent workflows.
None surface systematic loss patterns automatically.
None close the loop from RL improvement to causal business proof.
They are LLM observability tools being applied to an agent problem
they were not designed for.
Context.ai proved the market.
OpenAI acquired them because evaluation for AI systems
was too strategically important to leave independent.
That left their customers without a solution.
Here is why we build the moat they couldn't:
I spent 7 years at Google building evaluation systems and
causal inference frameworks for production AI.
I have observed firsthand that nobody measures agent accuracy,
nobody does loss pattern analysis, and nobody connects agents to business value.
That is the problem AgentIQ was built to solve.
Every customer we sign generates evaluation signal
that sharpens our judges — a flywheel that compounds
against every competitor entering this space."
P
Priya
Founder & CEO, AgentIQ
Staff Data Science Manager at Google · 7 years.
Led data science across Image Search, Google Lens, and Nest.
Built LLM evaluation systems and causal inference frameworks
for production AI at Google scale.
Deep expertise in experiment measurement —
PSM, Double ML, Diff-in-Diff, causal inference —
applied to proving real AI system impact in production.
Previously founded Instagen AI, an agent-building platform.
Google · 7 Yrs
LLM Evaluation
Loss Pattern Analysis
Causal Inference
Causal Inference
Nest LLMs
Image Search
Staff DS Manager