The agents keep running. The problems keep growing.
You've deployed AI agents into production.
The quality layer is missing.
You've deployed AI agents into production, but without a quality layer,
failures compound silently across workflows — no visibility into loss patterns,
no signal to improve, no feedback loop to fix what's breaking.
The agents keep running. The problems keep growing.
62%
Of enterprises already have AI agents live in production — Sinch 2026
32%
Cite quality as the #1 barrier to production — not cost, not capability
74%
Have rolled back a live AI agent after a governance failure — Sinch 2026
// Sinch Research · "The AI Production Paradox" · May 2026
Survey of 2,527 senior decision makers across 10 countries.
62% already have AI agents live in production. 74% have rolled one back
after a governance failure — not in testing, in front of real customers.
The rollback rate climbs to 81% among organizations with the most mature
guardrails. More governance, more monitoring, more investment —
and still eight in ten of the most advanced programs had to shut something down.
Governance doesn't prevent failures. It detects them after the fact.
Meanwhile, Palantir CEO Alex Karp told CNBC on July 1, 2026 that enterprises are
"livid — paying for tokens that create no value."
Experiment frameworks can measure the overall return. But without a quality layer,
nobody knows what is driving that return, what is dragging it down,
or what to fix to make it better.
// Cyera Research · September 2023 – May 2026
Cyera analyzed 7,200 publicly reported AI incidents.
188 cases of direct organizational harm caused by autonomous AI agents
in live enterprise production environments.
Deleted databases. Unauthorized financial operations. Runaway API spend.
Silent integrity corruption. In most cases —
the agent was invisible in the postmortem.
// Market Research Pipeline · November 2025
Four AI agents in production. Two entered an infinite loop.
11 days. $47,000 in API costs. Nobody noticed.
The team had monitoring. The loop never appeared in the sample.
No quality layer means failures compound silently
Existing tools sample traffic and check only final outputs. Failures that originate mid-workflow propagate silently across the agent harness with no quality signal to surface them. By the time anyone notices, the damage is already compounding.
No loss patterns means no feedback loop to improve
Without a quality layer, there is no signal to identify loss patterns, no input for RL improvement cycles, and no way to prove to leadership that agents are delivering return. Projects get canceled not because agents can't work — but because nobody can show they are working or getting better.
1
Connect — one line of code, any framework
Add agentiq.trace() to any agent —
LangChain, CrewAI, or any custom build. No rebuilding required.
AgentIQ starts observing every interaction the moment it's connected.
2
Every interaction scored — quality defined by industry, refined by the developer
AgentIQ ships with pre-configured quality standards by industry —
healthcare agents are scored on accuracy and safety,
coding agents on correctness and completeness,
retail agents on task resolution and tone,
marketing agents on relevance and brand alignment.
Developers can adjust weights and thresholds for their specific use case.
Quality scores work on day one. No configuration required to start.
3
See every failure — the moment it happens, not after the customer does
Every failure surfaces in the dashboard the moment it happens.
One-off catastrophic failures appear immediately.
Repeating patterns are grouped automatically with root cause.
Not "your agent has a 23% failure rate" —
"Billing disputes fail because the payment API times out at step 3,
happening since last Tuesday."
Developers fix in minutes. Not hours.
03
Why Existing Tools Miss This
Palantir CEO Alex Karp told CNBC on July 1, 2026 that enterprises are
"livid — paying for tokens that create no value."
Causal inference and experiment frameworks can measure the overall return.
But without a quality layer, nobody can see what is driving that return,
what failure patterns are dragging it down, or what to fix next.
Every tool below either samples, scores final outputs only,
or lacks the loss pattern detection and feedback loop that tells you what to improve.
Existing tools — LangSmith · Braintrust · Maxim · Arize
Observability and traces — not a quality layer
Trace debugging and latency monitoring
Sample production traffic to control costs
Score final outputs — miss mid-workflow failures
No loss pattern detection across the agent harness
No industry-specific quality standards
No feedback loop — same failures repeat
Cannot prove whether AI spend is generating value
Galileo acq. Cisco 2026. Context.ai acq. OpenAI 2025. Gaps still open.
AgentIQ · 2026
The quality layer — loss patterns, feedback loop, structured improvement signal
Every interaction evaluated — nothing sampled
Quality score per interaction — 0 to 100, calibrated by industry
Loss patterns surface automatically as they form
Every confirmed failure feeds the improvement loop
Developers define quality for their specific agent
Structured eval export — RL-ready JSON
Company
What They Do
100% Coverage
Loss Patterns + Feedback Loop
LangSmith
LangChain · $125M raised
Traces and debugs agent workflows. Added Insights Agent for pattern clustering in 2026. Strong inside LangChain ecosystem. Samples traffic. No loss patterns. No feedback loop for RL.
Samples
None
Braintrust
Well funded · Growing
AI observability platform. Strong evals and Brainstore custom DB for scale. Three pillars: observability, evals, automation. Samples production. No loss patterns. No industry quality defaults.
Samples
None
Maxim AI
Growing fast
Claims end-to-end lifecycle: simulation, evals, observability. Closed-loop architecture. Complex, enterprise sales motion. Samples production. No industry quality defaults. No RL feedback loop.
Samples
Partial
Confident AI
YC · Emerging
Evaluation-first. Full trace coverage. Research-backed metrics. Strong on human feedback workflows. No industry-specific defaults. No loss pattern grouping. No RL feedback loop.
Full
None
Microsoft Foundry
Launched Build 2026
Cross-framework tracing and eval launched June 2026. OpenTelemetry-based. Significant threat for Azure teams. No loss patterns. No industry defaults. No feedback loop.
Partial
None
Galileo
Acquired · Cisco 2026
GenAI evaluation and hallucination detection. Acquired by Cisco April 2026. Customers need a replacement. Gap is open.
Acquired
Gap open
AgentIQ ✦
Seed · 2026
The quality layer. Loss patterns surface automatically. Every confirmed failure feeds the RL loop. Industry defaults out of the box. Experiment frameworks measure the overall return — AgentIQ tells you why it is what it is and what to fix.
100%
Core
AgentIQ uses an LLM judge to evaluate every agent interaction the moment it happens.
The judge is not a generic LLM call. It is a precisely structured evaluation prompt —
built around the specific ways production agents fail, tuned to industry-specific
quality standards, and calibrated against real confirmed failures.
The technology is accessible. The calibration is not.
Calibration requires a dataset of real production failures that takes months to accumulate
and cannot be purchased, scraped, or replicated from scratch.
The loss pattern dataset is proprietary and grows with every deployment
Every production failure AgentIQ evaluates and confirms becomes a calibration point
in a dataset no competitor has. After 50 customers AgentIQ has seen real failure patterns
across billing disputes, refund flows, order management, and customer service —
at a breadth no single team accumulates internally.
That dataset trains progressively sharper loss pattern detection.
A competitor launching today lacks the tool and the 18 months of
real failure data needed to match the accuracy.
Quality configuration is institutional knowledge that lives inside AgentIQ
When a team configures AgentIQ for their specific agent —
defining what goal_alignment means for their flow,
setting failure thresholds for their compliance requirements,
tuning weights for their industry —
that configuration is not a file they export.
It is calibration built on weeks of confirmed failures specific to their agent.
Switching means rebuilding all of it from scratch.
The competitor's judge won't score the same failures the same way
because it hasn't seen those failures.
The feedback loop builds institutional memory no new tool can replicate
Every confirmed failure feeds the next evaluation cycle.
After 90 days, AgentIQ knows a specific agent's failure modes
better than any engineer who wasn't there for those 90 days.
A new tool starts blind.
AgentIQ starts with full memory of every failure that agent has ever had,
every pattern that emerged, and every fix that worked.
That memory cannot be transferred. It has to be earned.
The agentic AI market grows from $7B in 2025 to $93B by 2032 at 44.6% CAGR
(MarketsandMarkets, corroborated by Fortune Business Insights, Precedence Research, and others —
forecasts vary in scale but all point the same direction).
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
You've deployed AI agents into production, but without a quality layer,
failures compound silently across workflows — no visibility into loss patterns,
no signal to improve, no feedback loop to fix what's breaking.
The agents keep running. The problems keep growing.
62% of enterprises already have AI agents live in production.
74% have rolled one back after a governance failure — in front of real customers.
The rollback rate climbs to 81% among organizations with the most mature guardrails.
More governance, more monitoring — and still the same silent failures.
Because governance without a quality layer only tells you after the damage is done.
A team ran agents in production for 11 days with a $47,000 loop
that never appeared in their observability sample.
No loss patterns surfaced. No feedback loop fired. No signal to improve.
AgentIQ deploys alongside existing agents in minutes.
It evaluates every interaction at every step in real time —
not a sample, not after the fact.
It surfaces both repeating failure patterns
and one-off catastrophic failures the moment they happen.
High quality agents aren't a nice-to-have.
They're what determines whether agent deployments survive or get canceled.
Gartner predicts 40% of agentic AI projects will be canceled by 2027
due to unclear ROI and inadequate risk controls.
AgentIQ is how teams prove their agents are working
— and fix them when they're not.
Bear Pitch · For the Hard Room
"I know what you're thinking.
LangSmith has pattern clustering. Maxim has simulation.
Microsoft just launched agent eval at Build 2026.
The space looks covered.
Here is the gap none of them fill.
Every one of these tools either samples production traffic
or checks only the final output.
That architecture cannot find failures that originate mid-workflow —
the failures that define agent quality in production.
You can't see step 3 breaking from step 7's output.
You need to evaluate every step, in sequence, with full context.
Nobody does that. AgentIQ does.
The proof: a team had LangChain monitoring running
on four agents for 11 days.
$47,000 in API costs. The monitoring showed the agents were running.
It did not show which step entered the loop.
That is the gap — and it is structural, not a missing feature.
Sinch's May 2026 survey of 2,527 enterprises found the same pattern at scale:
81% rollback rate among organizations with the most mature guardrails —
higher than the overall average. More governance does not mean fewer failures.
It means better detection after the damage is already done.
I spent 7 years at Google building exactly this methodology —
evaluation systems that find where production AI fails,
at every step, at scale.
That is not on a benchmark paper. It is encoded into AgentIQ.
Every deployment sharpens our judges.
That flywheel is the moat."
P
Priya
Founder & CEO, AgentIQ
Staff Data Science Manager at Google · 7 years.
Built LLM evaluation systems and production AI quality frameworks
for Image Search, Google Lens, and Nest — at the scale of billions of interactions.
The evaluation methodology inside AgentIQ is the same methodology
Priya built and ran at Google, applied to the agent quality problem
no enterprise tool has solved.
Previously founded Instagen AI, an agent-building platform.
Google · 7 Yrs
LLM Evaluation
Loss Pattern Analysis
Causal Inference
Nest LLMs
Image Search
Staff DS Manager
Raising $500K pre-seed to ship the MVP, acquire the first 10 design partners,
and publish the production agent failure taxonomy that establishes AgentIQ as
the research authority in this space.
Pricing: usage-based SaaS — per interaction evaluated,
with a flat monthly floor per agent.
Causal inference proves AI ROI at the outcome level.
AgentIQ adds the layer underneath — showing which failure patterns are driving
poor outcomes, which improvements moved the needle, and what to fix next.
Together they close the loop from agent deployment to proven business impact.