Coforge Limited a global AI-native engineering services leader, today introduced its intent engineering framework, a practical design methodology that helps enterprises build agentic AI systems aligned to business outcomes, governance requirements, and operational realities.
As enterprises move autonomous AI agents into customer-facing and operational workflows, the consequences of misaligned decisions become increasingly significant. While prompt engineering influences responses and context engineering improves decision quality, neither guarantees that agents optimize for the outcomes businesses actually value. Coforge believes that intent engineering bridges that gap and is rapidly becoming a critical requirement for enterprise-scale AI adoption.
Detailed in a new thought leadership article, The 3 Layers of Enterprise AI: From Prompts to Context to Intent, Coforge’s intent engineering framework provides a structured approach for defining an agent’s purpose, boundaries, success metrics, governance controls, and escalation pathways.
“Most enterprises have invested heavily in teaching AI what to say and what it should know,” said Vic Gupta, Executive Vice President, Coforge. “The harder challenge is deciding what the agent should optimize for when objectives conflict, risk increases, or exceptions occur. Intent engineering creates the governance structure for those decisions before agents operate autonomously at scale.”
The framework reflects lessons learned from real-world expertise gained from executing enterprise AI programs for clients. Rather than evaluating AI agent performance by completion, speed, or productivity metrics, Coforge’s intent engineering approach introduces outcome-based design principles that prioritize governance, quality, accountability, and business impact, ensuring that AI autonomy translates into measurable value rather than simply increased activity.