Impetus Introduces Context Engineering Framework to Drive Enterprise AI Agent Adoption

As enterprises accelerate the integration of autonomous software agents into business operations, Impetus Technologies Inc. is framing corporate context management as essential infrastructure to ensure artificial intelligence tools deliver tangible organizational value.

Speaking in an interview with SiliconANGLE’s livestreaming studio theCUBE, Impetus Chief Growth Officer and Head of AI Deepak Khosla explained that the core bottleneck facing enterprise AI deployment is not the underlying capacity of foundational large language models. Instead, the primary barrier lies in the context gap—the divide separating generic model intelligence from the specific operational rules, workflows, and internal data of an individual business.

To address this challenge, the enterprise data and analytics company has structured an operational blueprint known as the Context Engineering Delivery Lifecycle, or CEDL. According to Khosla, traditional software development and legacy IT deployment models fail to meet the requirements of agentic AI systems, necessitating a specialized methodology focused on context creation, engineering, continuous learning, and feedback loops.

Under the CEDL methodology, Impetus establishes a formal context framework by modernizing legacy corporate data platforms while simultaneously constructing semantic layers and knowledge graphs to map complex relationships between business entities. The system also establishes structured ontology layers across organizational tools and implements distinct short-term and long-term memory architecture for AI agents.

Managing agent memory remains a critical technical component of the framework, Khosla emphasized to SiliconANGLE. Because underlying language models retain learned behaviors, failing to isolate or correct erroneous contextual data can cause mistakes to persist across subsequent automated actions and decisions executed by the agents.

Impetus incorporates these principles into its proprietary Leap AI product suite, which aims to transition autonomous agents from pilot environments into active enterprise production. Khosla likened contextualizing an AI agent to onboarding a highly qualified human recruit, noting that even the most capable worker requires explicit instructions regarding corporate rules and proprietary processes to perform effectively.

By embedding context engineering directly into data platform migration and infrastructure modernization projects, the company seeks to provide enterprise clients with an adaptable operational framework. The strategy ensures that autonomous systems receive necessary domain knowledge as foundational data platforms are updated, reducing failure rates in real-world software deployments.