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Agentic AI Buyer's Guide
Buy AI confidently — not on hype
The State of the AI Agent Market
The agentic AI market is maturing rapidly, but buyer sophistication lags vendor sophistication. Most companies are evaluating AI solutions for the first time without clear criteria. This guide gives you the evaluation framework that experienced AI buyers use to separate real capability from vendor theater.
What to Evaluate in an AI Agent Platform
Evaluate AI agent platforms across six dimensions: integration capability (can it connect to your actual systems?), reasoning quality (does it handle exceptions and edge cases well?), governance tools (can you monitor, audit, and control agents?), security architecture (how is your data protected?), implementation support (who builds and maintains the agents?), and total cost of ownership (what does this actually cost over 3 years?).
Questions That Separate Good AI Vendors from Great Ones
Ask every AI vendor these questions: What percentage of your customers are live in production vs. in pilot? Can you provide three customer references in our industry with similar use cases? What does your AI do when it's uncertain — how does it handle exceptions? Who maintains the agent after deployment? What is the SLA for when agents fail? The answers reveal operational maturity.
Red Flags to Watch For
Watch for these red flags: demonstrations on vendor data only (not your data), pricing structures that escalate sharply with usage, proprietary data models that create vendor lock-in, no clear governance or monitoring tooling, implementation partners who aren't the same team that built the platform, and outcome guarantees that aren't backed by contractual accountability.
FAQ
Frequently asked questions
For most middle market companies, buying an AI platform and customizing agents is more efficient than building from scratch. Building makes sense for proprietary processes where differentiation is critical and you have the engineering capacity.
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