Real-data test set creation
Build test sets from unstructured documents or knowledge bases, or turn production traces into evaluation sets so tests stay grounded in real usage.
AgentX
AgentX provides a production-focused framework for evaluating AI agents and LLMs with layered scoring, trace analysis, drift detection, and release gating. It supports real-data test sets, multi-step runs, and self-serve or enterprise purchasing options.
AI Agent Evaluation Framework for production LLMs
AgentX’s AI evaluation framework is a production-oriented system for evaluating AI agents and LLMs before and after deployment. The page positions it as an observability and traceability layer for agents, with support for building test sets from real data, running evaluations, and monitoring results over time.
The framework focuses on how agents behave in realistic workflows, not just whether they produce a correct answer on a single turn. It highlights multi-step and multi-run evaluation, layered scoring, drift detection, and CI/CD-style gating so teams can block releases when evaluation results fall below a threshold and promote changes when they pass.
The source also shows a broader product workflow around evaluation: create datasets from production traces or uploaded content, run evaluations, inspect step-level reports, compare model behavior with multiple LLM judges, and apply fixes before rerunning tests. Pricing information indicates AgentX is available through self-serve plans for builders and teams, as well as a sales-led enterprise offering with evaluation and deployment support.
Core capabilities
Multi-run and multi-step evaluation
Run repeated evaluations across multi-step workflows to measure consistency rather than relying on a single pass or single-turn answer.
Layered evaluation framework
Evaluate agents with four layers: task correctness, tool and API reliability, reasoning and consistency, and business and user impact.
Regression and KPI-linked scoring
Use benchmark and regression suites, plus metrics tied to business KPIs such as completion rate and user satisfaction, to connect eval results to release decisions.
Drift monitoring
Detect prompt and dataset drift and use alerting to monitor changes before and after deployment.
Trace-based analysis
Review execution timelines, tool actions, and traces across agent steps to understand where a failure occurred and what to change.
Practical use cases
Pre-release quality checks
Teams can evaluate whether an agent completes business tasks correctly before shipping, using layered scoring instead of a single accuracy number.
Dataset building from real usage
Product and platform teams can create evaluation datasets from production traces, then keep those tests aligned with how users actually interact with the agent.
Failure analysis and debugging
Engineering teams can inspect multi-step runs, tool calls, and execution timelines to identify where a workflow breaks and what to fix.
Ongoing production monitoring
Organizations using agents in production can monitor drift after deployment and use alerts or threshold breaches to trigger another evaluation cycle.
Model comparison and review
Teams comparing agent or model behavior can use side-by-side evaluation and multiple judges to reduce reliance on a single scoring model.
Pros and Cons
Pros
- Designed for production evaluation rather than demo-only testing, with support for continuous evaluation before and after deploy.
- Covers multiple evaluation layers, including task correctness, tool reliability, reasoning quality, and business impact.
- Supports real-data datasets from production traces, documents, and knowledge bases, which helps ground tests in actual usage.
- Includes step-level trace analysis and reports for multi-step, multi-agent runs, making failures easier to inspect.
- Connects evaluation results to release decisions through thresholds, regression suites, and CI/CD-style gating.
Cons
- Some implementation details are only described at a high level, so supported connectors and exact workflow integrations are not fully documented in the provided sources.
- The source gives limited concrete examples of end-to-end customer scenarios beyond the evaluation workflow itself.
FAQ
What does AgentX’s AI evaluation framework measure?
It evaluates AI agents and LLMs in production using layered metrics such as task correctness, tool and API reliability, reasoning and consistency, and business and user impact. The site also describes continuous evaluation, regression suites, drift detection, and A/B results.
How does the evaluation workflow work?
The site describes a workflow that starts with building a test set, then running an evaluation, scoring and surfacing failures, making a threshold decision, iterating or deploying, and monitoring drift afterward.
Can it evaluate multi-step or non-deterministic agent workflows?
The homepage says it supports multi-run and multi-step evaluation, including repeated runs and workflows with multiple interactions, which helps account for non-deterministic agent behavior.
Is AgentX available as a self-serve product or only through sales?
The pricing page shows self-serve plans for builders and teams, plus a sales-led enterprise option. It also mentions API access, multi-agent workflows, and demo evaluation mode on the self-serve plans.
Does AgentX use real production data or synthetic test cases?
The source emphasizes production traces and real datasets as the preferred foundation, while also noting that synthetic generation can be used when coverage gaps exist.
Quick Facts
- Category
- AI evaluation
- Product
- AgentX
- Primary focus
- AI agent and LLM evaluation in production
- Workflow
- Build test set → run evaluation → score failures → decide threshold → deploy or iterate → monitor drift
- Availability
- Self-serve plans and enterprise sales option
- Domain
- agentx.so
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