From real datasets
Create test sets from unstructured data. Synthesize ground truth from documents or knowledge bases. Continuously enrich your data assets to ensure your evaluations stay accurate, relevant, and up to date.
AI agent evaluation
Evaluate AI agents before they fail.
AgentX provides the AI observability and traceability you need to evaluate AI agents and serve as a reliability guardrail.
Multi-run & multi-step
Measureing consistency with repeated runs. Assess multi-step workflows with multiple interactions. AgentX evaluation embraces the non-deterministic nature while providing reliable metrics.
From evals to agent CI/CD
Don't just run evals. Use them to build a CI/CD pipeline for your agents. Automatically block deployments if evals fail, or promote to production if they pass. Update and deploy your AI agents with confidence.
Continuous evaluation loop
Runs before deploy and continuously after
01
Build test set
02
Run evaluation
03
Score & surface failures
04
Threshold decision
05
Iterate or deploy
06
Monitor drift
On threshold breach → loop back to Run evaluation
Monitor, analyze, fix
Deploy agents with confidence
Developers need to know what to fix. So AgentX analyzes agent behavior to pinpoint issues, surface hidden patterns, and prescribe fixes.
Monitoring
AI Agent setup
- LLM
- Prompts
- Skills
- Memory
- Knowledge Base
- Doc Parsing
- Tools
- MCPs
- Workflow
- Chain-of-Thought
Analyze
Execution Timeline
Timeline{} JSON
Search
PhaseStepDelegateToolActionMCPTODODetail
1
Initialization
Main Phase · 517ms
517ms
2
Preprocessing
Main Phase · 75ms
75ms
3
Attachment Processing
Main Phase · 11μs
11μs
4
Onboarding Retrieval
Main Phase · 534ms
534ms
5
Knowledge Retrieval - Initialization
Detailed Phase · 248ms
248ms
6
Knowledge Retrieval - DOC
Detailed Phase · 284ms
284ms
7
Prompt Assembly
Main Phase · 594μs
594μs
8
ReAct Loop
Main Phase · 5.44s
5.44s
9
Step 1: LLM Call
Execution Step · 5.09s
5.09s
10
Post-processing
Main Phase · 123ms
123ms
78.8%vector similarity21.3%jaccard similarity
Fix
Failure detected
3.0/10.0
Hallucinations caused baseless assumptions that led to incorrect decisions.
Suggested fix applied
Restrict the assumption in the system prompt. Add few-shot examples to demonstrate the correct thinking process.
Resolved
9.8/10.0
Claude Sonnet 4.6Input2,210Output9690.263 Judges
Framework
Four layers of AI agent & LLM evaluation
A strong LLM evaluation framework goes beyond accuracy. These layers cover production AI and LLM testing end to end.
- 01
Task correctness
Did the agent complete the task correctly? Essential for any LLM evaluation—beyond single-turn accuracy.
- 02
Tool & API reliability
Did tools run as expected? Latency, errors, and correctness of tool outputs. Critical for AI agent evaluation when agents use tools.
- 03
Reasoning & consistency
Multi-step reasoning quality, coherence, and consistency across runs. Key LLM evaluation metrics for production.
- 04
Business & user impact
User satisfaction, completion rate, and downstream business KPIs. The top layer of a full agent evaluation framework.
Built for production
Production-ready LLM evaluation framework
AgentX gives you an AI agent evaluation framework built for production: continuous LLM evaluation, regression and benchmark suites, and LLM evaluation metrics tied to business outcomes. Evaluate AI agents at scale.
- Continuous LLM evaluation in production
- Regression and benchmark suites for AI agent testing
- LLM evaluation metrics tied to business KPIs
- Prompt and dataset drift detection and alerting
Product
From test set to fix
Everything you need to evaluate, and improve your AI agents, end to end.
01Create
Build test sets from real data - or just drag & drop
Turn production traces into evaluation sets in one click, or drag and drop documents, files, and content to synthesize test cases. Your evals stay grounded in what users actually do.
02Evaluate
Run an evaluation and get a score in seconds
Kick off an evaluation on any agent and instantly see a numerical score across your test set - so you know exactly how it performs before you ship.
03Analyze
Read a full report across every step and agent
Inspect evaluation reports generated from multi-step, multi-agent runs - with every action, tool call, and data layer traced, scored, and explained.
04Judge
Multiple LLM judges, less model bias
AgentX evaluates with a panel of LLM-as-a-judge models from different vendors, so no single model's bias skews your results. Consensus scoring you can trust.
05Fix
One-click fixes, then re-run until it's green
Apply suggested changes with a single click and re-run the evaluation - iterating until every test passes and your agent is ready to deploy.
Release process
Operationalize AI agent evaluation
Integrate LLM evaluation into your release process: run before deploy, monitor in production, iterate with a consistent evaluation framework.
1
Define metrics
Choose layers and KPIs that map to your goals.
2
Run continuously
Evaluation on every change and in production.
3
Act on signals
Drift alerts, A/B results, and regression gates.
Wire it into your own stack with the Python SDK: record traces from LangChain, CrewAI, or OpenAI Agents, score runs against test datasets, and gate merges on a pass rate threshold.
FAQ
AI agent evaluation FAQ
Common questions about evaluating AI agents to be production-ready.
What is AI agent evaluation?
+
AI agent evaluation is measuring how well your AI agents or LLMs perform in production - beyond demos. It includes task correctness, tool reliability, reasoning quality, and business impact (completion rate, user satisfaction, drift detection, A/B testing).
How do you evaluate LLMs in production?
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Evaluate LLMs in production with a layered framework: (1) task correctness, (2) tool and API reliability, (3) reasoning and consistency, and (4) business and user impact. Use continuous evaluation, regression suites, drift detection, and metrics tied to KPIs like completion rate and user satisfaction.
Why is AI agent evaluation hard?
+
AI agent evaluation is hard because agents are non-deterministic, use tools and memory, and perform long-horizon, multi-step reasoning. Prompt drift and dataset drift make traditional accuracy metrics insufficient. You need an AI evaluation framework built for production.
Are you generating synthetic test cases, or do you rely on real production traces? Synthetic evals often miss the edge cases users actually trigger.
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We agree. Production data is usually the best source of truth. Our focus is helping teams build evals from real traces and failure cases, while also supporting synthetic generation when coverage gaps exist. The best results come from combining both approaches.
What's the most surprising thing teams learn from side-by-side LLM provider comparisons, and which model is most reliable for enterprise agents?
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One common surprise is that the most expensive model isn't always the best for a given task. We've seen teams reduce costs significantly while maintaining quality, and we've also seen latency become a bigger issue than model accuracy for some workflows. Sometimes it's really case by case when you need to consider both quality and price. So run it yourself and you will know.
When an agent fails, how often is the root cause the model versus the prompt, tools, retrieval layer, or workflow design?
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Much less often than people think. In many cases the model is only one piece of the puzzle. Failures often come from missing context, retrieval issues, tool execution problems, or orchestration logic. That's why we focus on full traceability instead of just model outputs.
What does a failed deployment look like in AgentX? Can teams set quality thresholds that block releases?
+
Exactly. Teams can define evaluation criteria and quality thresholds. If a change causes performance regressions, the evaluation can fail before deployment, similar to how software teams use automated tests to prevent bad releases.
From the blog
Evaluation in practice
From building datasets to running evaluations and turning metrics into business value. Step-by-step guides from the AgentX blog.
Part 1
Create evaluation datasets
Building enterprise-grade evaluation datasets: the foundation of reliable AI agents. Realistic test cases, expected results, capabilities, and follow-ups.
ReadPart 2Run AI agent evaluation with the dataset
From dataset to decision: running enterprise AI agent evaluations. Select your agent and dataset, run the evaluation, and get results with justifications and performance metrics.
ReadPart 3Turn metrics into business value with evaluation analysis
How to analyze, interpret, and act on AI agent evaluation results. Root-cause analysis, suggested instruction changes, re-runs to validate - turn evaluation into a release process.
ReadEvaluate AI agents in production. Stop the guesswork.
Use the AgentX AI agent evaluation framework to turn your LLMs and agents from demos into measurable, production-grade systems.