“Agentic AI is not just infrastructure,” Gogia said. “It’s behavioral autonomy encoded in software. When agents act unpredictably, or when standards drift from implementation, the consequences are not limited to system bugs. They extend into legal exposure, operational failures, and reputational damage.”
Su agreed that alignment is possible but not guaranteed. “Aligning major vendors around shared governance, APIs, and safety protocols for agents is realistic but challenging,” Su said, citing issues like rising expectations and regulatory pressure.
Sheel said early indicators of progress will include wider production use of MCP and AGENTS.md, cross-vendor governance guidelines, and tooling for auditability and inter-agent communication that works consistently across platforms: “We’ll know it’s working when enterprises can use these tools and safety controls at scale, not just in proofs of concept.”
