Evaluating Agent Products
The market is flooded with products calling themselves "AI agents." Some are genuine autonomous systems. Others are chatbots with a new label. This chapter gives you the framework to tell the difference and make informed purchasing decisions.
The Marketing vs. Reality Gap
AI agent marketing tends to follow a pattern:
- Demo: A polished video showing the agent completing a complex task flawlessly
- Reality: The agent handles 60% of cases well, struggles with 30%, and fails on 10%
- What they do not show: The setup time, the edge cases, the human oversight required, the cost per interaction
This does not mean agent products are scams. It means the gap between the demo and daily reality is wider than in most software categories. Your job is to estimate the size of that gap before you buy.
Questions That Cut Through the Hype
About the Core Technology
What model powers the agent? Vendors who will not tell you are hiding something. The model choice reveals the capability ceiling.
Can you swap models? Vendor lock-in to a single model provider is a risk. If that provider raises prices or degrades quality, you want options.
How does it handle failures? Ask for specific examples of agent failures and how the system recovers. "It rarely fails" is not an answer.
About the Tools and Integrations
What can the agent actually do? Get a complete list of tools and actions. "Integrates with your stack" is vague. "Can read from Salesforce, write to Jira, and send Slack messages" is specific.
What are the permission boundaries? Can the agent delete data? Send external communications? Modify configurations? Know exactly what the agent can do, not just what it should do.
About Performance and Reliability
What is the success rate on real-world tasks? Not on curated benchmarks — on actual production workloads similar to yours.
What is the average cost per completed task? Including model costs, tool costs, and the cost of human oversight for failed cases.
What is the latency? How long does the agent take to complete a typical task? Multi-step agents can be slow.
About Data and Security
Where does your data go? Does the vendor's model provider see your data? Is data used for training? Where is it stored?
How is sensitive information handled? Can the agent access customer PII? What prevents data leakage between users?
What compliance certifications does the vendor hold? SOC 2, GDPR, HIPAA — depending on your industry, these matter.
The Pilot Framework
Never deploy an agent product broadly without a pilot. Structure your pilot to answer three questions:
1. Does it actually work? Run the agent on real tasks for 2–4 weeks. Measure success rate, failure types, and edge cases.
2. Is it economically viable? Track total cost of ownership — licensing, model costs, integration effort, and human oversight.
3. Do users accept it? If the people who interact with the agent do not trust it or find it frustrating, adoption will fail regardless of the technology.
Red Flags
Be cautious when you see:
- No free trial or pilot period. If the vendor will not let you test with your data, why not?
- Vague metrics. "Saves hours of work" without specifics. "AI-powered" without explaining what the AI does.
- No human oversight option. Any vendor that tells you their agent needs no human review is either dishonest or reckless.
- Rapid feature claims. "We can build anything" usually means "We have not built much yet."
- No error handling story. If they cannot explain what happens when the agent fails, they have not thought about it.
Build vs. Buy
For organizations with technical teams, the build vs. buy decision is real:
Buy when: The problem is generic (support, research, content), speed to deployment matters, and your team lacks agent expertise.
Build when: The problem is domain-specific, data sensitivity prevents sharing with vendors, and you need deep customization.
Hybrid: Buy the platform, build custom tools and integrations on top of it. This is often the pragmatic middle ground.
For a broader look at the build vs. buy decision in AI, see AI for Non-Technical Leaders — The Build vs Buy Decision.