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Evaluating AI Agents

How to Evaluate an AI Agent: A Guide for Customer Service Leaders

Insights from Fin Team•

AI Agents are now the frontline of customer service. Modern agents understand intent, retrieve knowledge, follow policies, and execute multi-step workflows to fully resolve issues across every channel.

Selecting the right AI agent directly impacts:

  • Resolution rates
  • CSAT and brand experience
  • Support team efficiency
  • Operating costs
  • Long-term scalability

This guide gives you a vendor-agnostic framework to evaluate any AI agent, run fair head-to-head tests, and choose a system that delivers real business value.

How This Guide Is Structured

To make evaluation clear and predictable, the guide is divided into six parts:

  1. The Evaluation Framework
  2. Entry Criteria (Can the agent even work for you?)
  3. Evaluation Criteria (How well does it work?)
  4. How to Build a Good Test
  5. How to Evaluate the Vendor
  6. Post-Launch Optimization

You can read straight through or jump to the sections most relevant to your organization.

Part 1: The Evaluation Framework

Evaluating an AI agent requires a structure that prevents guesswork and avoids relying on vendor claims.

This guide uses a two-tier model:

Tier 1 — Entry Criteria

Determines whether the agent can operate in your environment:
✔ Capabilities
✔ Platform fit
✔ Security/compliance
✔ Self-manageability

Tier 2 — Evaluation Criteria

Determines whether the agent performs well:
✔ Resolution
✔ Automation
✔ Quality
✔ Experience
✔ Cost impact

Result:
You get a balanced view of technical viability and real-world performance.

Part 2: Entry Criteria (Viability Check)

Before comparing performance, confirm the agent is viable for your stack, your use cases, and your team’s workflow.

There are three viability questions:

2.1 Can the AI Agent Support Your Use Cases?

Focus on whether the agent can handle what your operation actually needs.

Core Capabilities Checklist

Your AI agent should support:

Complex, multi-step workflows

  • Clarification questions
  • Deductive reasoning
  • Procedural flows

Personalization with data

  • CRM lookups
  • Billing or order status
  • Conditional answers

Action execution

  • Refunds
  • Cancels
  • Subscription edits
  • Account changes
  • API-driven tasks

Behavioral control

  • Tone
  • Guardrails
  • Escalation rules
  • Fallback logic

Omnichannel + multilingual

  • Chat, email, voice, SMS
  • Social channels
  • 40+ languages minimum

Insights + analytics

  • Identify gaps
  • Recommend improvements

Seamless handoff

  • Invisible transitions to humans

At-a-Glance: Why Capabilities Matter

Capabilities only matter if they enable accurate, autonomous, end-to-end resolution — not just deflection.

2.2 Can the AI Agent Operate in Your Environment?

Platform fit ensures the agent functions securely, integrates with your systems, and is future-proof.

Integration Requirements

Check compatibility with:

  • Helpdesk
  • Knowledge base
  • CRM
  • Internal APIs
  • Billing or order systems
  • Analytics

Extensibility

Look for:

  • APIs
  • SDKs
  • Webhooks

Security & Compliance

Confirm:

  • GDPR, CCPA
  • HIPAA (if needed)
  • SOC 2 / ISO 27001 / ISO 42001
  • SSO, RBAC
  • Audit logs
  • PII controls

At-a-Glance: Why Platform Fit Matters

If the agent cannot integrate securely or reliably, nothing else matters — performance will break downstream.

2.3 Can Your Team Manage and Improve the Agent Without Vendors?

This is the most important predictor of long-term success — and the most overlooked.

Questions to Ask

Can your team:

  • Build workflows without engineering?
  • Adjust tone, rules, and guardrails?
  • Update knowledge instantly?
  • Run simulations before deploying changes?
  • Configure multi-step workflows and API actions?
  • Ship improvements within minutes?
  • Iterate without vendor tickets?

What Good Looks Like

A self-managed AI agent enables:

  • No-code workflow creation
  • Immediate knowledge updates
  • Behavior and tone controls
  • Safe testing environments
  • Multi-system data connections
  • Channel-specific deployments
  • Daily iteration

Red Flags

Avoid systems that require:

  • Vendor engineers
  • Professional services
  • Long, opaque change cycles
  • Limited visibility
  • No simulation or safe testing

At-a-Glance: Why Self-Manageability Matters

Your AI agent becomes a digital employee.
If you can’t train it yourself, you lose:

  • speed
  • flexibility
  • quality
  • ROI

Part 3: Evaluation Criteria (Performance Check)

Once viability is confirmed, test real-world performance using real conversations.

There are two performance lenses:

3.1 Business Performance

These metrics determine whether the agent saves time and money.

Core Metrics

  • Resolution Rate — Did the AI solve the issue?
  • Involvement Rate — How often did the AI engage?
  • Automation Rate = Resolution × Involvement — Your true ROI metric
  • Time Saved — Manual hours eliminated
  • Cost Per Resolution — AI vs human
  • Experience Score / CSAT — Did customers like it?

3.2 Conversation Quality

How well does the AI communicate?

Quality Dimensions

  • Accuracy — Understanding and retrieval
  • Behavior — Tone, policy adherence, escalation
  • Experience — Smoothness and clarity

At-a-Glance: Why Quality Matters

High resolution with poor experience leads to churn; high experience with poor resolution wastes time.

You need both.

Part 4: How to Build a Strong AI Agent Test

Every AI agent should be evaluated using the same criteria and the same dataset.

The Blueprint’s recommended process:

Step 1: Define Success

Agree on goals for:

  • Resolution
  • Accuracy
  • Behavior
  • Experience

Step 2: Build a Realistic Test

Use real customer data and include:

  • Multi-step workflows
  • Vague prompts
  • Urgent/emotional cases
  • Multiple languages
  • Typos and broken grammar
  • Multi-turn clarifications
  • Edge cases

Step 3: Score Performance

Use the same rubric for all vendors.

Measure:

  • Business performance
  • Conversation quality

Step 4: Make a Decision

Compare:

  • Performance
  • Quality
  • Platform fit
  • Vendor strength
  • Long-term alignment

Part 5: Evaluate the Vendor, Not Just the Agent

A powerful AI agent is useless without a strong vendor supporting it.

Key Vendor Qualities

  • Vision — Are they leading or reacting?
  • Transparency — Do they set realistic expectations?
  • Support — Do they help beyond onboarding?
  • Track Record — Do similar companies succeed with them?

Why It Matters

AI agents become part of your service strategy.
You need a vendor who can scale with you.

Part 6: Post-Launch Optimization: Your AI Ops Model

A great AI agent keeps improving.

Your operating loop becomes:
Train → Test → Deploy → Analyze

Evaluate whether vendors support:

  • Training workflows
  • Simulations and test environments
  • Behavioral controls
  • API connectors
  • Analytics
  • Continuous iteration

This forms your AI Ops cadence and determines long-term success.

Conclusion: Choose an AI Agent That Resolves, Scales, and Improves

Selecting an AI agent requires understanding:

  • what it can do
  • whether it fits your environment
  • how it performs
  • how it communicates
  • how it evolves
  • and whether the vendor can support you long-term

As AI becomes the core of customer service, the right agent will drive real resolution, scalability, and operational agility.

If you’re ready to build an AI-first support model, explore the Fin AI Agent Blueprint.


To see what action-capable AI looks like in practice, book a live demo of Fin.