Building enterprise-ready agentic AI
Recent work at QuantumBlack Labs, McKinsey’s AI innovation and engineering group, highlights four capabilities that are becoming essential for enterprise-ready agentic AI: flexible architectures, rigorous evaluation, disciplined engineering practices, and scalable, encoded expertise.
The QuantumBlack, AI by McKinsey blog on Medium underscores four emerging priorities:
- Architecture. Flexible platform architectures can help organizations navigate the “gen AI paradox,” making technology decisions that address immediate business needs without limiting future opportunities. Future-ready platforms are designed to integrate internal systems, external tools, and reusable AI services while maintaining the flexibility to adapt as models and agent frameworks continue to evolve.
- Evaluation. Agentic systems introduce new sources of uncertainty, from tool failures and memory drift to unexpected behaviors across multiagent workflows. Organizations increasingly need evaluation frameworks that assess performance, reliability, and risk at the system level, helping build confidence as AI systems become more autonomous.
- Engineering. Organizations must also recognize that many gen AI initiatives struggle to scale beyond the demo stage because they’re approached as isolated experiments rather than engineered systems. Achieving sustainable impact requires well-designed agentic workflows, clear human oversight, disciplined engineering practices, and operating models that make AI reliable, measurable, and production-ready. This enables humans and agents to work together effectively and deliver long-term business value at scale.
- Expertise. To scale gen AI effectively, organizations must shift from hard-coding instructions to treating their domain knowledge as a reusable asset. By developing agent skills that package proven methodologies and decision logic, companies can structurally extend their agents’ capabilities on demand. This approach transforms human expertise into a centrally governed advantage, ensuring consistent, expert-level performance across enterprise workflows.
Together, these capabilities help organizations build AI systems that are flexible, reliable, operationally scalable, and increasingly capable of leveraging institutional expertise. As agentic AI continues to evolve, organizations that invest in these foundations will be better positioned to capture lasting business value.
To learn more, explore the latest insights from QuantumBlack, AI by McKinsey on Medium.
