Evaluating AI Agents in 2026: The Race Has Shifted from Capability to Reliability
G2 has introduced its first-ever "Best Agentic AI Software" awards category, and the timing says as much as the rankings do. The decision to create a dedicated evaluation framework for agentic systems — backed by a survey of over 1,000 business decision-makers — is itself a signal that the market has reached a stage of maturity where buyers need structured criteria, not just demos. Salesforce Agentforce leads the inaugural list, but the more important finding may be the shift in how enterprises are asking questions about these tools.
According to G2's research, 57% of companies are already running AI agents in production environments. The dominant buyer question has moved from "Can this agent complete a task?" to "Can this agent complete a task reliably, within a real workflow, under real governance constraints?" Capability is now table stakes. The differentiator is predictability — agents that behave consistently across edge cases, integrate cleanly with existing systems, and operate within compliance boundaries that enterprise legal and security teams will actually sign off on.
G2's framing of agents as a capability layer within the stack rather than a standalone product category is particularly worth noting. It reflects where the practical engineering conversation has already arrived: frameworks like LangChain, AutoGen, and CrewAI are increasingly infrastructure choices, not product bets, and the evaluation criteria for them should reflect that. For teams building internal evaluation rubrics, this report is a useful external benchmark.