The Coforge intent engineering framework, introduced by Coforge Ltd on October 5, 2026, aims to help enterprises build AI agents aligned with business outcomes, governance requirements and operational realities, according to a BSE filing.

The company described the framework as a practical design methodology for enterprises moving from AI experimentation to autonomous execution. It addresses how agents should make decisions as they enter customer-facing and operational workflows, where misalignment with business objectives can carry greater consequences.

Coforge said prompt engineering influences what AI systems say, while context engineering improves the quality of their decisions. Neither, it argued, guarantees that agents optimise for the outcomes a business actually values. Intent engineering is intended to address that gap by defining objectives and decision boundaries before autonomous deployment.

Defining purpose, controls and escalation

The framework sets out a structured approach covering several elements of AI agent design:

  • The agent’s purpose and operating boundaries.
  • Success metrics linked to intended outcomes.
  • Governance controls governing autonomous decisions.
  • Escalation pathways for situations requiring intervention.

Vic Gupta, Executive Vice President, Coforge, said enterprises had invested heavily in teaching AI what to say and what information it should know. The harder challenge, he said, is deciding what an agent should optimise for when objectives conflict, risks increase or exceptions arise.

Gupta said intent engineering creates the governance structure for those decisions before agents operate autonomously at scale. The approach therefore places the definition of priorities and controls within the design process.

Outcome-based evaluation of AI agents

Coforge said the framework draws on experience gained from executing enterprise AI programmes for clients. Its outcome-based design principles prioritise governance, quality, accountability and business impact, rather than evaluating agent performance through completion, speed or productivity metrics alone.

The stated aim is to translate AI autonomy into measurable business value instead of simply increasing activity.

The methodology is detailed in Coforge’s thought leadership article, The 3 Layers of Enterprise AI: From Prompts to Context to Intent, available on the company’s blog. Coforge submitted the release to BSE and NSE under the applicable SEBI listing disclosure provisions.