TLDR: Skan AI has released its Agentic Process Automation Manifesto, a set of six core principles aimed at guiding the successful implementation of AI agents in large enterprises. The manifesto emphasizes an ‘Observe, Then Automate’ approach, leveraging Skan AI’s Observation-To-Agent (O2A) platform to create a ‘living blueprint’ of human work for AI agents. This initiative seeks to overcome the limitations of traditional automation by providing AI with the necessary context for complex, multi-step processes, ultimately enhancing efficiency, compliance, and customer experience across various industries.
MENLO PARK, Calif. – October 15, 2025 – Skan AI, a leader in enterprise process intelligence, has officially published its Agentic Process Automation Manifesto, a groundbreaking document outlining six battle-tested principles for deploying effective AI agents within complex enterprise environments. This strategic release aims to address the critical challenges organizations face in harnessing the full potential of artificial intelligence for operational transformation.
The manifesto’s principles are derived from over fifty real-world deployments across major enterprises in sectors such as banking, healthcare, insurance, and services. Skan AI asserts that while large enterprises are eager for AI that can reason and act, a significant hurdle remains: AI agents often lack an accurate, comprehensive understanding of how work is truly performed.
Manish Garg, Co-founder and Chief Product Officer of Skan AI, articulated this challenge, stating, “Every large enterprise wants AI that can reason and act. The blocker is that agents lack an accurate picture of how work is actually performed. The missing piece is the living system of record of process execution.”
Skan AI’s distinctive approach, dubbed ‘Observe, Then Automate,’ is central to the manifesto. Their Observation-To-Agent (O2A) platform meticulously observes human interactions with digital systems – capturing every click, keystroke, workaround, and judgment call across an organization’s entire technology stack. This continuous digital footprint is then transformed into a ‘living blueprint’ that AI agents can follow to execute intricate, multi-step processes from inception to completion. This method provides AI agents with full context regarding process variations, compliance requirements, and the inevitable exceptions that often derail traditional automation systems.
According to Skan AI, current ‘agentic’ approaches frequently fall short. Task-first methods are often local and inflexible, failing to adapt to minor variations and optimizing only individual tasks rather than end-to-end outcomes. Database-first approaches, while providing insights, lack the execution capability for complete workflows and rely on static data that quickly becomes outdated. Platform-first solutions are limited by their own boundaries, unable to observe and automate work that occurs outside their specific ecosystem, such as on desktops, vendor portals, or legacy UIs.
The adoption of agentic AI, as highlighted by industry experts, is poised to reshape business operations. Gartner predicts that by 2028, at least 33% of enterprise software will depend on agentic AI, underscoring the necessity for robust, orchestrated workflows to overcome the current high failure rates in AI agent implementations. Agentic AI allows for the seamless integration of AI models, human-in-the-loop tasks, and governance controls within a single process, leading to competitive advantages, new operating models, and significant efficiency gains.
Skan AI’s process intelligence technology is designed to capture a 360-degree view of operational processes, creating a ‘living digital twin.’ This digital twin, combined with what Skan AI refers to as Large Action Models (LAMs), enables process agents to understand and navigate domain-specific operations, contextual nuances, process variants, exceptions, and permutations. This comprehensive understanding is crucial for building smart, scalable agents that can drive complex processes effectively.
By systematically capturing work patterns and encoding them into intelligent automation, organizations can achieve accelerated processing, ensure stringent regulatory compliance, and significantly enhance customer experiences, all while maintaining complete control over data privacy and operational risk. This paradigm shift elevates automation from handling simple, repetitive tasks to managing complex, variable workflows that demand judgment and context, ultimately augmenting human potential rather than replacing it.
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Fortune 500 enterprises are already deploying Skan AI’s solutions to transform operations across diverse industries and use cases, including service delivery, claims processing, underwriting, and financial operations, solidifying Skan AI’s position at the forefront of enterprise process intelligence.
