For ML engineers building production agents
Your agent runs.
You find out it broke at step 3
when the customer tells you.
AgentIQ scores every step your agent takes — in real time, in production.
You see exactly where quality dropped, catch loops before they cost money,
and give your agent memory of past failures so it doesn't repeat them.
Works with LangChain, CrewAI, AutoGen, and any custom framework. One line to instrument.
import agentiq
agent = agentiq.trace(your_agent, industry="healthcare")
The problems you're solving manually right now
🔍
You debug failures from customer reports
A failure at step 3 that only surfaces at step 7 looks like a final output problem.
You spend hours in traces working backwards to find the root cause.
By then the customer has already moved on.
🔁
Loops burn money before you notice
Monitoring shows your agents are running. It doesn't show two of them entered
a tool-call loop at step 1.
$47K in API costs over 11 days.
Nobody noticed until the bill arrived.
📉
You score the final output and miss mid-workflow failures
LangSmith and Arize were built for LLM observability, not agent quality.
Sampling 10% of traffic means 90% of failures are invisible.
Scoring only the final output means you can't locate which step broke.
🧠
Your agent repeats the same mistake next interaction
There's no mechanism to warn an agent about a failure pattern before it hits
the same step again. Fixing it requires retraining —
which needs labeled data you don't have yet.
1
Install
pip install agentiq
Works with LangChain, CrewAI, AutoGen, LlamaIndex, or any custom agent framework.
No vendor lock-in. No agent rebuild.
2
Wrap your agent
agent = agentiq.trace(your_agent, industry="retail")
Pass your existing agent object. Set an industry for out-of-the-box scoring defaults —
healthcare, retail, coding, or marketing.
That's it. AgentIQ starts evaluating the moment the agent runs.
3
Tune (optional)
Override default scoring dimensions and thresholds via config or the dashboard.
Set alert channels — Slack, email, webhook.
Adjust Failure Memory confidence thresholds.
Nothing is required to start getting scores and alerts on day one.
What you get once it's running
Per-step scoring · Real-time
Know exactly which step broke — not just that something did
Every step gets a quality score the moment it completes. A drop at step 3 shows up at step 3, not when the customer complains. Drill into any step from the dashboard in one click.
Score drops from 91 → 44 at step 3. Root cause: payment API returned null. Time to locate: 8 seconds.
Loop detection · Deterministic · Free
Loops flagged before they cost you money
Step repetition, tool call loops, and runaway API spend are caught automatically on every trace. No LLM call. No added latency. Alerts fire via Slack, email, or webhook immediately.
Loop detected at step 1. Alert fired at 09:14. API spend stopped at $2.40 — not $47,000.
Failure Memory · No retraining required
Your agent stops repeating the same mistakes
Past failures are retrieved and injected into your agent's context before each new interaction. The agent sees what has gone wrong in similar flows — and avoids it. No fine-tuning, no prompt editing, no model update.
"Billing flows: step 3 payment timeout in 34% of cases. Add timeout fallback before calling payment API."
Loss patterns · Auto-grouped
Recurring failures named and tracked automatically
Failures that share the same step, failure mode, and context type are grouped into named patterns. Each shows frequency, first seen, and last seen. You see trends, not noise.
"payment_api_timeout_step3" — 47 occurrences — first seen Jun 3 — last seen Jun 10.
Training data export · JSONL · Fine-tune ready
Your production failures become your fine-tuning dataset
Every scored failure is saved as a labeled example: step, context, score, outcome. Export as JSONL whenever you're ready to fine-tune or run RL. NeurIPS 2025 (ReasonRAG): step-level scored data requires 18× less training data to converge than outcome-only labels. Compatible with OpenAI fine-tuning, HuggingFace, and custom pipelines.
| Tool |
Per-step scoring |
Loop detection |
Failure Memory |
Training data export |
| LangSmith |
✗ |
✗ |
✗ |
Partial |
| Arize |
✗ |
✗ |
✗ |
✗ |
| Maxim AI |
✗ |
✗ |
✗ |
✗ |
| Galileo (acq. Cisco) |
Output only |
✗ |
✗ |
✗ |
| AgentIQ ✦ |
✓ Every step |
✓ Built in |
✓ Built in |
✓ JSONL |
What this means for your enterprise
AgentIQ is built for the engineer. But the quality data it generates solves problems for the whole org.
Risk & Compliance
Know what your agent is doing in production — in real time
Every interaction scored and logged. Anomalies flagged immediately. Audit trail built automatically.
Product & Stakeholders
Prove your agents are working — with data
Quality scores over time. Failure rate trends. Step-level breakdowns. Reports your PM can show the business.
Engineering & Ops
Stop debugging blind
Root cause located to the step, not the interaction. Loss patterns named and tracked. MTTR drops from hours to minutes.
Ship agents you can actually trust in production.
Early access open for teams building agents on LangChain, CrewAI, AutoGen, and custom frameworks.