You're not ready for AI agents—unless you can answer these 10 questions first
According to a McKinsey report, 92% of companies plan to increase their AI investments over the next three years. However, only 1% say they have fully integrated their deployments. The rest are stuck in pilot or limited deployment purgatory.
We've entered the era of AI agents —AI-powered software that is autonomous, acts on behalf of people, takes initiative, and makes decisions. The AI agent market is heating up as companies are racing to sell agents across domains: legal, finance, healthcare, logistics, insurance, and more.
In addition to asking questions about the acquisition and operations cost, the quality of the model underlying AI agents, and the resources needed for deployment, here are 10 questions every smart buyer and enterprise team should get answers to before deploying an AI agent at scale:
1. IS YOUR DATA READY FOR REAL-TIME DECISIONS?
AI agents rely heavily on data for decision-making, making data quality and accessibility paramount. Legacy systems often store data in silos with inconsistent formats, which hinders AI integration. The agent can't wait on batch jobs or manual input, either. If it is expected to act in real time, upstream systems must deliver clean, current data without delay.
2. WHAT'S DONE TO OVERCOME THE COLD START PROBLEM?
Agents learn over time—but most vendors hand you a blank slate. Ask if the agent comes with pretrained patterns, out-of-the-box policies, or environment bootstrapping. If every deployment starts at zero, your pilot will stall.
3. WHAT KIND OF AUTONOMY DOES THE AGENT REALLY HAVE?
Can it actually trigger actions, make decisions, and learn—or is it just sending suggestions to a human? Ask where autonomy starts, where it stops, and what rules govern that boundary. You're not buying a chatbot. You're buying an actor.
4. HOW DOES THE AGENT INTEGRATE, AND HOW WELL DOES IT HANDLE FAILURE?
Basic API calls don't cut it. Does it preserve context across queries? Ask how the agent handles system unavailability, retries, identity mapping, and broken workflows. Can it resume or recover its state after an interruption? If it's meant to collaborate with other agents, does it use a structured message protocol or just broadcast?
5. HOW IS TRUST EARNED—AND REVOKED—AT THE SYSTEM LEVEL?
You wouldn't give an employee root access to every system. Don't do that with agents. Ask whether it supports enterprise SSO. Can you assign it specific roles via identity and access management? Does it sign its actions? If an agent can't be trusted to act under a scoped identity, it shouldn't act at all.
6. WHAT'S THE PLAN FOR AMBIGUITY, CONFLICTING SIGNALS, OR MISSING DATA?
You'll want to know if the agent has policy fallback logic—or if it stalls. And if it's in a multi-agent system, ask how it escalates or delegates using structured communication protocols (MCP, A2A, etc.). Agents that can't coordinate get stuck or overstep. If you're deploying multiple agents, you're introducing concurrency. Build rules for which agent owns what state. Don't assume they'll play nicely by default.
7. IS THE PROCESS STABLE—OR ARE YOU HANDING THE AGENT A MOVING TARGET?
Don't automate chaos. If the workflow isn't documented, or changes weekly, the agent won't succeed. Especially when multiple agents are involved. Model context protocol only works when protocols don't change midstream.
8. WHO OWNS POST-DEPLOYMENT BEHAVIOR?
Autonomy doesn't mean independence. Someone on your team needs to monitor agent outcomes, tune policies, and intervene when things drift. Otherwise, you'll lose trust the first time the agent goes off script.
9. DO YOU HAVE OBSERVABILITY IN PLACE?
AI agents consume computing resources that impact both performance and cost. Monitor CPU utilization (maintaining below 80% for optimal performance), memory consumption (keeping below 90% to prevent crashes), and API success rates (targeting at least 95% successful calls). These metrics help identify potential bottlenecks before they impact user experience and provide insights for resource allocation decisions.
10. HOW CUSTOMIZABLE ARE THE AGENTS?
Business requirements evolve, and AI agents must adapt accordingly. Prioritize solutions offering high degrees of customization to meet specific organizational needs. This includes the ability to tailor prompts, workflows, knowledge bases, and integration points without requiring extensive development resources.
In my experience, most AI agent projects don't fail in production—they fail in the gap between proof of concept and real enterprise-wide deployment. That's where most companies stall. It's not because they didn't invest. It's because they moved forward without answering the hard questions. If you want to avoid pilot purgatory, skip the hype and get clear on how the agent will operate, fail, recover, and evolve in your environment. Start there—or don't start at all.
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