AI agents flood the market. Some deliver 10x productivity gains. Others sit unused after week one.

The difference isn't luck. It's evaluation. The businesses succeeding with AI agents share a common trait: they ask the right questions before signing contracts.

This guide provides a complete evaluation framework. Use it to separate agents that work from agents that waste money.

Section 1: Problem-Fit Assessment

The most common mistake: buying an agent before defining the problem.

Questions to Answer

What specific task will this agent handle?

Not "improve customer service" but "respond to tier-1 support tickets within 5 minutes."

Who currently does this task, and how long does it take them?

Document the baseline. You'll need it for ROI calculation.

What does success look like?

Faster completion? Fewer errors? Lower cost? Higher volume? Define metrics.

Is this task repetitive and rule-based, or does it require creative judgment?

AI agents excel at high-volume, consistent tasks. Creative judgment requires human-in-the-loop approaches.

How often does this task happen?

Daily tasks with high frequency show faster ROI than weekly or monthly tasks.

Understanding what makes a task suitable for AI agents versus other solutions matters here. Our guide on [AI agent vs chatbot differences](link to your existing blog) clarifies which technology fits different use cases.

Evaluation Criteria

Score each criterion 1-10:

Task specificity: How clearly defined is the workflow?

Measurability: How easy is it to track success metrics?

Volume: How frequently does the task occur?

Consistency: How predictable is the task pattern?

Current pain: How significant is this problem today?

Red Flags

You struggle to articulate the specific problem

The task changes significantly week to week

Success depends heavily on subjective quality assessments

The workflow involves too many exceptions and edge cases

No baseline metrics exist for comparison

Section 2: Capability Verification

Demos are designed to impress. Real performance matters.

Questions to Ask Vendors

Does the agent work with our specific tools and platforms?

Get specific. Name every system the agent must connect to.

What's the error rate on tasks similar to ours?

Request data from comparable customers, not cherry-picked success stories.

How does the agent handle edge cases it hasn't seen before?

Every workflow has exceptions. Understand the failure mode.

What happens when the agent fails?

Does it escalate to humans? Queue for review? Stop processing?

Can we test with our own data before buying?

Any vendor refusing this request has something to hide.

What's the accuracy rate in production environments?

Demo accuracy differs from real-world accuracy. Get production numbers.

Testing Protocol

Request a trial period with these parameters:

Duration: 2-4 weeks minimum. Enough time to see patterns.

Volume: 50-100 real tasks from your actual workflow.

Data: Recent real-world examples, not sanitized test cases.

Edge cases: Include exceptions, not just clean examples.

Users: 3-5 team members with different experience levels.

Metrics to track during testing:

Completion rate: Percentage of tasks finished without intervention

Accuracy rate: Percentage correct on first attempt

Speed comparison: Agent time vs human baseline

Failure patterns: Which task types cause problems

User feedback: Team experience with the agent

Red Flags

Vendor refuses trial with your actual data

Demo uses only cherry-picked examples

No clear explanation of failure handling

Accuracy claims lack supporting documentation

No existing customers in your industry or use case

Section 3: Integration Requirements

An agent that doesn't connect to your existing systems creates more work, not less.

Compatibility Checklist

List every tool the agent must connect to:

CRM system

Email platform

File storage

Databases

Communication tools

Industry-specific software

For each integration, verify:

Native integration exists, OR

API access is available, AND

Authentication works with your security policies

Data Flow Questions

Where does the agent store data?

On-premise, cloud, or hybrid? Which region?

How does data move between systems?

Real-time sync, batch processing, or manual export?

Can you export data if you switch providers?

Avoid vendor lock-in through proprietary formats.

What permissions does the agent require?

Minimum necessary access vs administrative privileges.

Integration Effort Estimation

For each required integration:

Native integration: 1-2 hours setup

Documented API: 4-8 hours development

Custom integration: 20-40+ hours development

Multiply development hours by developer cost. Add to total cost of ownership.

For detailed guidance on integration planning, read our guide on [how to implement AI agents](link to your existing blog).

Red Flags

No native integration with core platforms

Integration requires expensive custom development

Vendor locks you into proprietary data formats

API documentation is incomplete or outdated

No SSO or enterprise authentication support

Section 4: Security and Compliance

Your legal and IT teams will ask these questions. Have answers ready before they block the purchase.

Security Checklist

SOC 2 Type II certification (minimum for enterprise)

Data encryption at rest and in transit

Role-based access controls

Audit logging and activity monitoring

Penetration testing documentation

Incident response procedures

Vulnerability management program

Employee security training

Request documentation for each item. Verbal assurances aren't sufficient for compliance.

Compliance Verification

GDPR compliance (if handling EU customer data)

Industry-specific requirements:

HIPAA for healthcare

PCI DSS for payment data

FERPA for education

SOX for financial reporting

Data processing agreements available

Clear policy on AI training with your data

Data residency options for geographic requirements

For a comprehensive security evaluation framework, read our guide on [AI agent security risks](link to your existing blog).

AI-Specific Security Questions

Is customer data used to train the model?

Many AI providers use customer data for model improvement. Understand the policy.

Can we opt out of data training?

Enterprise customers often require this option.

How is prompt injection prevented?

AI-specific attacks require AI-specific defenses.

What happens to data after contract termination?

Retention and deletion policies matter.

Red Flags

No security certifications

Unclear answers about data handling

Agent trains on your data without explicit consent

No data processing agreement available

Security documentation unavailable before purchase

Section 5: Total Cost Analysis

Subscription price is never the full cost.

Direct Costs

Monthly or annual subscription fee

Per-task or per-API-call charges (understand volume implications)

Tiered pricing thresholds and overage rates

Additional user seats or departments

Premium support packages

Professional services for implementation

Hidden Costs

Calculate internal resource requirements:

Implementation time: Project manager hours x hourly cost

Technical setup: Developer hours x hourly cost

Integration work: Additional development if needed

Training: Team hours x hourly cost

Ongoing maintenance: Monthly admin time x hourly cost

Productivity dip: Transition period reduced output

Total First-Year Cost Formula

Total Cost = Subscription + (Implementation Hours x Rate) + (Training Hours x Rate) + Custom Development + Productivity Dip Estimate

Compare this against projected value from ROI calculation. Ensure positive return even in conservative scenarios.

For detailed ROI calculation methods, read our guide on [the AI agent ROI formula](link to your ROI blog).

Pricing Model Analysis

Per-seat pricing: Scales with team size. Good for stable teams.

Usage-based pricing: Scales with volume. Watch for unexpected spikes.

Flat-rate pricing: Predictable costs. May overpay at low volume.

Tiered pricing: Step changes at thresholds. Plan around tier boundaries.

Red Flags

Pricing unclear or constantly changing

Usage caps that don't match your volume needs

Long-term contracts with no performance guarantees

Hidden fees discovered after signing

No trial or pilot pricing available

Section 6: Support and Reliability

When the agent breaks at 9am on Monday, response time matters.

Support Checklist

Support hours and time zone coverage

Response time SLAs by severity level

Support channels (email, chat, phone)

Dedicated account manager for enterprise

Self-service documentation quality

Community forums or user groups

Onboarding assistance included

Training resources available

Reliability Verification

Published uptime statistics (target: 99.9%+)

Public status page with incident history

Historical downtime patterns

Disaster recovery procedures

Scheduled maintenance windows

Backup and data recovery capabilities

Evaluate the Evidence

Check the status page history. How many incidents in the past year? How long did they last? Were customers notified promptly?

Read customer reviews mentioning support. Response time claims differ from actual experience.

Ask for customer references. Talk to existing customers about their support experience.

Red Flags

No SLA or vague uptime commitments

Support only via email with 48+ hour response

No public status page or incident history

Recent major outages without clear resolution

Customer reviews consistently mention poor support

Section 7: Scalability and Future-Proofing

Your needs today will change. The agent should grow with you.

Growth Questions

What happens when task volume doubles?

Does pricing scale linearly or geometrically?

Can the agent handle multiple departments or use cases?

Expansion potential without new procurement.

Is there a product roadmap with planned improvements?

Signals ongoing investment vs maintenance mode.

How often does the agent receive updates?

Regular updates indicate active development.

What's the typical implementation timeline for new features?

Customer-requested features: months or years?

Vendor Viability Assessment

Company age and funding status

Customer count and retention rate

Employee growth trajectory

Competitive position in market

Partnership ecosystem strength

The AI agent market is projected to reach $47.1 billion by 2030 according to Grand View Research. Vendors positioned in growing segments have stronger long-term viability.

For market context, read our guide on [5 AI technologies driving the $52B AI agent economy](link to your existing blog).

Red Flags

No clear scaling path

Vendor has no roadmap visibility

Last product update was months ago

High customer churn or negative reviews

Funding concerns or layoff news

The 7-Point Scoring System

Rate each section 1-10 based on your evaluation. Calculate total score.

Problem-Fit Assessment: /10

How well does the agent match your specific workflow?

Capability Verification: /10

Did testing demonstrate reliable performance?

Integration Requirements: /10

How smoothly does the agent connect to your systems?

Security and Compliance: /10

Does the agent meet your security standards?

Cost Analysis: /10

Is total cost of ownership acceptable?

Support and Reliability: /10

Will you get help when you need it?

Scalability and Future-Proofing: /10

Will the agent grow with your needs?

Total Score: /70

Scoring Interpretation

60-70 points: Strong buy. Move forward with implementation planning.

50-59 points: Proceed with caution. Address weak areas before deployment.

40-49 points: Significant concerns. Explore alternatives or negotiate improvements.

Below 40 points: Pass. Keep looking for better options.

Weighted Scoring Variation

If certain factors matter more for your organization, apply weights.

Example for compliance-heavy industry:

Security and Compliance: 2x weight

Support and Reliability: 1.5x weight

Adjust based on your priorities.

Evaluation Process Timeline

Week 1: Problem Definition

Document workflow details

Establish baseline metrics

Define success criteria

Identify stakeholders

Week 2: Initial Research

Identify 3-5 candidate agents

Review documentation and pricing

Eliminate obvious mismatches

Week 3-4: Vendor Evaluation

Request demos and trials

Submit security questionnaires

Check customer references

Week 5-6: Testing

Run trials with real data

Measure against success criteria

Gather user feedback

Document issues

Week 7: Decision

Score each candidate

Compare total cost of ownership

Select winner or extend evaluation

Week 8: Negotiation

Finalize pricing and terms

Confirm implementation support

Establish success metrics in contract

Building Your Shortlist

Start with agents designed for your specific use case.

Browse AI agents on sundae_bar marketplace organized by business function. Filter by industry, workflow type, and integration requirements.

The marketplace shows performance data and customer reviews. Use this information to build your initial candidate list before requesting trials.

For guidance on what to look for in a marketplace, read our [AI agent marketplace 2025 guide](link to your existing blog).

Testing Before Commitment

sundae_bar enables testing agents before purchase. Run your evaluation protocol with real data. Compare observed performance against your scoring criteria.

The trial period reveals issues that demos hide. Integration complexity, edge case handling, and user experience become clear with actual usage.

Invest evaluation time upfront. The cost of choosing wrong exceeds the cost of thorough assessment.

Common Evaluation Mistakes

Mistake 1: Skipping the Trial

Demos show best-case scenarios. Trials reveal real performance. Never commit without testing.

Mistake 2: Evaluating Alone

Include IT for integration assessment. Include legal for security review. Include end users for usability feedback. Solo evaluation misses critical perspectives.

Mistake 3: Rushing the Timeline

Pressure to deploy fast leads to poor choices. An extra two weeks of evaluation prevents months of regret.

Mistake 4: Ignoring User Feedback

The team using the agent daily knows what works. Their input predicts adoption success.

Mistake 5: Focusing Only on Features

Features mean nothing without workflow fit. A simpler agent that matches your process outperforms a complex agent that doesn't.

Making the Final Decision

Your evaluation score provides quantitative comparison. But some factors resist scoring.

Consider:

Team enthusiasm: Will users embrace this agent or resist it?

Vendor relationship: Do you trust this company as a partner?

Strategic alignment: Does this agent fit your technology direction?

Gut check: What does your experience tell you?

The best decisions combine rigorous evaluation with experienced judgment.

If your team shows resistance to AI adoption, our guide on [why your team resists AI agents](link to your existing blog) addresses common objections before they derail implementation.

After You Choose

Evaluation doesn't end at purchase. The first 90 days determine long-term success.

For a complete implementation roadmap, read our guide on [how to implement AI agents](link to your existing blog) covering deployment phases, training, and optimization.

Track the metrics you defined during evaluation. Compare actual performance against projections. Adjust or escalate if results fall short.

Getting Started

Browse AI agents on sundae_bar marketplace organized by business function. Use this checklist to evaluate candidates systematically.

Test before you buy. Score before you commit. The evaluation investment pays dividends through successful deployments and avoided failures.