Coforge Limited has introduced an intent engineering framework, a design methodology aimed at helping enterprises build agentic AI systems that are aligned with business objectives, governance requirements and operational needs.

The framework comes as companies increasingly deploy autonomous AI agents across customer-facing and business-critical workflows, where poorly aligned decisions can carry greater operational and financial risks.

According to Coforge, while prompt engineering helps shape how AI responds and context engineering improves the information available to agents, intent engineering focuses on defining what an AI agent should ultimately achieve and how it should operate when objectives conflict or unexpected situations arise.

The framework provides a structured approach to defining an agent’s purpose, operating boundaries, success metrics, governance controls and escalation mechanisms. It is detailed in Coforge’s latest thought leadership article, The 3 Layers of Enterprise AI: From Prompts to Context to Intent.

Coforge said its approach shifts the evaluation of AI agents beyond metrics such as speed, productivity and task completion towards outcome-based measures, with greater emphasis on governance, accountability, quality and measurable business impact.

The company added that the framework draws on its experience delivering enterprise AI programmes and is designed to help organisations scale AI autonomy while maintaining greater control over decision-making and risk.