How to Assess Multi-Domain AI Agents

Evaluating multi-domain AI agents presents unique challenges compared to single-domain models, as agents need to function seamlessly across diverse, complex environments where failures can cascade across interconnected systems.

The evaluation of these agents involves understanding their ability to operate consistently across multiple realms without succumbing to domain-specific pitfalls. Agents that perform well in isolated domains often falter when faced with cross-domain tasks.

These nine essential strategies for evaluating enterprise multi-domain AI agents will equip you with actionable insights for enhancing agent performance and reliability. Implementing these approaches is critical for avoiding disruptions, ensuring regulatory compliance, and harnessing the full potential of AI technologies in complex enterprise environments.

We recently explored this topic on our Chain of Thought podcast, where industry experts shared practical insights and real-world implementation strategies:

Strategy #1: Quantify tool selection accuracy

You've probably watched an otherwise solid agent derail because it called the wrong API or passed malformed arguments. One bad tool decision cascades through downstream steps, multiplies latency, and forces expensive human rollbacks.

Most teams try to catch these failures through manual log reviews, but they're always playing catch-up with production disasters.

You can use a tool selection quality metric to cut through this reactive cycle by scoring every tool invocation on three critical dimensions:

• Whether the agent chose the correct tool

• Whether its parameters match expert expectations

• How often the call fails at runtime

The metric uses LLM-as-a-judge with chain-of-thought prompts, providing explanations and binary judgments critical for agentic workflows. A perfect 1.0 means your agent consistently selects the right tool with valid arguments and zero technical errors.

Low TSQ scores surface as rising support tickets and compute bills. When you correlate TSQ drops with retry storms or aborted sessions, you spot planning bugs the moment they appear. Real-time dashboards surface these patterns, while per-domain breakdowns reveal specific teams or toolkits that need retraining.

Within minutes, you'll know which agent decisions deserve immediate attention—and which ones you can safely ignore while focusing on real problems.

Recent leaderboard analysis across 17 leading models reveals that top performers like Gemini-2.5-flash achieve 94% tool selection quality, while others struggle to exceed 70% on the same enterprise scenarios. This variation becomes critical when selecting models for production deployment.

Strategy #2: Validate cross-domain competence with a domain-coverage matrix

Imagine your agent crushes finance benchmarks, then crumbles the moment it encounters healthcare terminology. This happens because most teams test thoroughly in one domain, assume success translates everywhere, then discover costly blind spots in production.

Traditional validation misses these gaps entirely. Teams run comprehensive tests on legal documents, celebrate strong performance, then watch the same agent stumble through logistics schemas or clinical workflows.

Galileo's Agent Leaderboard v2 demonstrates exactly this challenge in practice. Our comprehensive evaluation across five enterprise sectors—banking, healthcare, insurance, investments, and telecoms—reveals dramatic performance variations that simple benchmarks miss entirely.

For instance, GPT-4.1 achieves 62% action completion overall but shows significant domain-specific variance, while Gemini-2.5-flash excels at tool selection (94% TSQ) yet struggles with actual task completion (38% AC) across complex scenarios. These performance gaps become invisible when teams evaluate only within single domains.

A domain-coverage matrix solves this challenge by mapping performance across every domain-task combination. Create a grid where rows represent domains—legal, logistics, clinical—and columns represent task types like classification or reasoning.

Fill each cell with domain-specific accuracy scores, track cross-domain consistency, and measure overall coverage breadth. This reveals whether your agent maintains steady performance or collapses when data distributions change.

Load curated datasets per domain, and the system scores every interaction while flagging weak performance cells.

Trend lines across releases show whether your agent is improving or regressing in specific domains, and one-click reporting gives stakeholders a clear map of strengths, gaps, and the domains requiring attention before production deployment.

Strategy #3: Measure task-completion reliability at scale

Missed or half-finished tasks are the silent budget killers in every agent deployment. You fix one broken flow, only to watch another resurface hours later, eroding user trust and piling up manual reprocess costs.

Large-scale studies on multi-agent systems confirm that orchestration complexity, not model accuracy, causes most production outages, especially when financial transactions hang mid-flow instead of closing cleanly.

Tracking task completion requires three critical metrics that work together to provide comprehensive visibility:

• Action completion measures whether your agents fully accomplish every user goal and provide clear answers for every request, tracking context across up to eight interdependent user requests

• Action advancement determines if your agent makes meaningful progress toward user goals during each conversation turn.

• Tool error detection identifies and categorizes failures when agents attempt to use external tools or APIs.

Adding safety layers that halt infinite retries before they hit your database provides another protection mechanism.

Strategy #4: Score response quality with automated evaluators

A single hallucinated figure or an answer that sounds off-brand destroys user trust instantly. Multi-domain deployments amplify this risk—medical jargon bleeding into retail conversations, or formal language where casual tone belongs.

Most teams try manual spot-checking, but you can't review thousands of daily responses without missing the subtle failures that matter most.

Effective quality assessment tracks three dimensions simultaneously:

• Conversation quality (logical flow and readability)

• Information relevance (actual answer accuracy)

• Brand voice alignment (company tone consistency)

This comprehensive approach catches problems that single-metric systems miss. You can leverage Galileo’s Luna-2 evaluator to automate this multi-dimensional scoring through specialized language models trained as evaluation judges.

These models apply structured reasoning—similar to the methodology used for TSQ assessment—before delivering scores with clear explanations. Fine-tune each evaluator for domain-specific terminology using minimal annotation examples through CLHF.

Then, configure alerts when quality metrics drop below acceptable thresholds so problematic responses never reach production.

Strategy #5: Monitor latency & efficiency in real time

Slow agents kill user experience and drain your compute budget. You might see decent response times during development, but production traffic reveals hidden bottlenecks—sub-agent handoffs that stall, memory leaks from incomplete cleanup, or orchestration overhead that compounds with scale.

Track the metrics that expose these problems across your entire system:

• Agent efficiency evaluates how effectively your agents utilize computational resources and time while maintaining quality outcomes.

• Prompt Perplexity measures how familiar prompts are to language models, helping identify prompt quality issues that cause delays

• Uncertainty quantifies how confident models are during token generation, identifying hesitation points that increase latency.

• Conversation quality assesses coherence across multi-turn interactions, revealing inefficient dialogue patterns

• Intent change monitoring identifies when user goals shift mid-conversation, often a source of wasted computation and extended sessions.

These readings distinguish between network delays, model processing time, and coordination failures—critical distinctions for multi-domain systems.

Real-time dashboards connect these performance metrics directly to cost-per-trace analytics. For instance, you get to see exactly how a 300ms delay translates to higher token spend and customer frustration.

Strategy #6: Stress-test safety & compliance boundaries

Fines under the EU AI Act can reach 7-figure territory, and a single rogue response from your agent is enough to trigger them. You've seen how quickly an agent can leak personal data or generate biased content—the reputational damage arrives long before the lawyers.

Offline audits help, but they miss fresh prompts and jailbreak tactics that surface only after deployment.

Effective protection starts with quantifiable targets that provide comprehensive safety coverage. Track safety and compliance metrics for every interaction. Probe weaknesses by hammering your staging environment with adversarial prompts that test profanity filters, PII redaction, and disallowed advice.

The goal isn't breaking the model once—it's mapping the failure edge so you can reinforce it.

Runtime guardrails make that reinforcement deterministic. A runtime protection API sits inline, applying policies the moment an agent produces output. Wire these systems into your orchestration loop, set hard overrides for finance or other use cases, and surface every violation to your existing observability portals.

With guardrail metrics feeding your CI/CD gates, releases stop automatically when compliance slips.

Strategy #7: Visualize decision paths across all domains

Raw traces from multi-agent workflows read like unbroken chat logs. You scroll for minutes, lose context, and still can't pinpoint why the refund step misfired. Trace volume spikes as soon as an agent team grows beyond five members, overwhelming traditional log viewers.

Galileo’s Graph Engine solves this by turning every interaction into a directed graph. Each node captures a decision or tool call, edges track information flow, and color coding flags failures. One glance replaces hundreds of log lines and transforms debugging from archaeological excavation into targeted diagnosis.

The graph reveals optimization opportunities beyond immediate triage. Recurring detours expose bloated tool chains; isolated dead ends reveal brittle APIs. By pruning those edges and fortifying weak nodes, you tighten the entire workflow before users feel the impact.

Effective graph visualization requires careful attention to the graph's information density and readability. When visualizations follow consistent design patterns, you can develop pattern recognition that speeds up anomaly detection—spotting unusual workflow branches becomes almost instinctive after reviewing just a few dozen traces.

Strategy #8: Standardize evaluation in CI/CD with an automated engine

You probably ship agent updates as frequently as any microservice, yet ad-hoc spot checks let silent regressions slip into production. Miss one faulty tool call and an otherwise healthy release can unravel—a pattern enterprise AI adoption reports consistently warn about regarding cascading failures in complex agent teams.

Embedding agent evaluation directly in your CI/CD pipeline eliminates that risk. Instead of trusting manual reviews, you turn every pull request into a miniature red-team exercise. 

To achieve this, you need an automated insight engine to generate comprehensive metrics—tool selection quality, task completion, latency, safety guardrails—and compare them against baselines from prior builds.

For example, when a change drags TSQ below 0.9 or triggers anomaly alerts, the pipeline fails before anything reaches users.

Root-cause summaries arrive with the failure report, so you know whether a bad parameter, slow external API, or drift in a domain-specific sub-model caused the regression.

Getting started requires three straightforward steps to integrate a comprehensive evaluation into your existing development process. Add evaluation actions to your workflow file, define pass/fail thresholds in a YAML policy, and export results to the same dashboard you already use for unit and integration tests.

From there, every merge automatically inherits guardrails, and every rollout comes with audit-ready evidence that your multi-domain agents still behave as intended.

Strategy #9: Benchmark against industry-standard leaderboards

Isolated testing creates blind spots that industry benchmarks expose. Your internal evaluation might show promising results, but how does your agent compare to leading models across standardized enterprise scenarios?

Public leaderboards provide essential context for performance expectations. Galileo's Agent Leaderboard v2 evaluates models across five critical sectors with realistic multi-turn conversations, revealing that even top-tier models like GPT-4.1 achieve only 62% action completion in complex enterprise scenarios.

Cost-performance analysis becomes crucial at scale. The leaderboard shows GPT-4.1-mini delivering exceptional value at $0.014 per session versus $0.068 for GPT-4.1, while maintaining competitive performance across domains—insights that internal testing alone cannot provide.

These benchmarks serve as early warning systems for production deployment. If industry-leading models struggle with specific domain combinations, your agent likely faces similar challenges that require architectural changes rather than prompt optimization.

Ship reliable multi-domain AI agents with Galileo

Every outage chips away at trust, and you've seen how a single misrouted tool call can ripple through production. Agent misfires remain a top blocker to enterprise AI adoption, accounting for a meaningful slice of unplanned downtime each year.

Ensuring agents’ reliability demands a platform that evaluates, protects, and improves agents in one continuous loop:

• Automated quality guardrails in CI/CD: Galileo integrates directly into your development workflow, running comprehensive evaluations on every code change and blocking releases that fail quality thresholds

• Multi-dimensional response evaluation: With Galileo's Luna-2 evaluation models, you can assess every output across dozens of quality dimensions—correctness, toxicity, bias, adherence—at 97% lower cost than traditional LLM-based evaluation approaches

• Real-time runtime protection: Galileo's Agent Protect scans every prompt and response in production, blocking harmful outputs before they reach users while maintaining detailed compliance logs for audit requirements

• Intelligent failure detection: Galileo’s Insights Engine automatically clusters similar failures, surfaces root-cause patterns, and recommends fixes, reducing debugging time while building institutional knowledge

• Human-in-the-loop optimization: Galileo's Continuous Learning via Human Feedback (CLHF) transforms expert reviews into reusable evaluators, accelerating iteration while maintaining quality standards

See how Galileo closes your agent reliability loop through integrated capabilities that address every aspect of agent reliability.