Agentic AI Evaluation
& Observability Platform
Trace. Evaluate. Govern. Improve.
Innodata helps evaluate, monitor, govern, and continuously improve enterprise AI agents before and after deployment with an expert-led AI agent evaluation and observability platform, human review workflows, custom frameworks, and audit-ready evidence.
AI agents are moving faster than governance can keep up
AI agents now retrieve data, call tools, make recommendations, and complete multi-step workflows across business systems. But many teams still lack a defensible way to know whether those agents are accurate, compliant, safe, and ready for production.
Most AI governance software reports what an agent did. Enterprise teams deploying agents in regulated workflows need to know why it did it, whether it should have, and whether they can prove it to an auditor.
That is where Innodata comes in.
From agent risk to validated performance
Innodata helps organizations move from unclear agent behavior to measurable performance, defensible governance, and continuous improvement before and after deployment.
Trace-level agent observability and failure analysis
Agents fail in ways dashboards do not explain. Trace workflows, tool calls, reasoning paths, and failure patterns to understand where agents break down and why.
Custom AI agent evaluation frameworks and rubrics
Generic metrics do not reflect your business risk. Define what "good" looks like using evaluation criteria aligned to your policies, workflows, regulations, users, and domain-specific requirements.
Pre-production agent testing and validation
Teams need proof before agents reach production. Test agents against real-world tasks, edge cases, policy expectations, and regulatory requirements before launch.
Continuous monitoring and regression detection
Production agents drift, regress, and change over time. Continuously monitor quality, safety, latency, drift, compliance, and workflow completion after launch.
Expert-led agent performance improvement
Signals need to become improvements. Use production insights, evaluation results, and expert review to refine prompts, workflows, rubrics, datasets, and agent behavior over time.
Audit-ready governance reporting and evidence
Stakeholders need evidence, not just logs. Generate outputs that support ship/no-ship decisions, governance reviews, audits, and model change validation.
Why Innodata for Enterprise AI Agent Governance
Most tools show what happened. Innodata helps you decide what to do next.
Automated evaluation alone is not enough for regulated, high-stakes AI workflows. Innodata combines evaluation technology with expert-led framework design, domain-specific criteria, human review, and audit-ready reporting so teams can move from raw signals to defensible deployment decisions.
AI Agent Observability Platform Comparison: Typical LLM Tools vs. Innodata
| Area | Typical LLM Observability Tools | Innodata Agentic Evaluation & Observability |
|---|---|---|
| Primary Focus | Token-level metrics, latency, generic quality scores | Agent-level reasoning, tool use, and workflow completion across full lifecycles |
| Configuration Burden | Heavy user configuration of metrics, dashboards, and checks | Pre-configured evaluation system, guided workflows, and expert-supported rubrics |
| Compliance & Audit | Limited or generic audit support | Audit-grade logging with GDPR, HIPAA, SOX experience and exportable reports |
| Business KPI Linkage | Technical metrics loosely connected to business goals | Rubrics co-designed with your teams to mirror operational and financial KPIs |
| Agent Orchestration Coverage | Limited multi-step and tool-using agent evaluation | Deep support for tool-chaining, orchestration quality, and multi-agent workflows |
| Vendor / Model Neutrality | Often tied to a particular stack or ecosystem | Fully model and vendor agnostic across frameworks and AI vendors |
| Services & Advisory | Product only, minimal evaluation design support | Embedded advisory on rubric design, failure analysis, and governance orchestration |
Built for teams that need to
prove AI is ready for production
Where AI Agent Deployment Assurance Matters Most
Financial Services: AI Agent Evaluation for AML, Fraud, and Onboarding Workflows
- Custom regulatory criteria
- Trace-level decision review
- Human oversight
- Continuous monitoring
- Audit-ready reporting
See Our AI Agent Evaluation Platform in Action
In this demo: how the platform traces an agent's decision path through a sanctions screening workflow, scores it against custom regulatory rubrics, flags a silent failure, and generates the audit-ready evidence a compliance reviewer needs.
Talk to an expert about your AI agent risk, readiness, and governance needs.
You’ll leave with a clear view of:
- Which agent workflows should be evaluated first
- What rubrics, benchmarks, or human review may be needed
- How to generate evidence for governance and ship/no-ship decisions
- What a tailored evaluation program could look like for your use case