MENLO PARK, Calif., Oct. 15, 2025 /PRNewswire/ — Skan AI today released its Agentic Process Automation Manifesto, a set of six battle-tested principles for building AI agents that actually work in the enterprise. These concepts are drawn from more than fifty real-world‑ deployments at some of the world’s largest enterprises in banking, healthcare, insurance, and services.
“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,” said Manish Garg, Co-founder and Chief Product Officer of Skan AI.
Skan AI’s Approach: Observe, Then Automate
Skan AI’s Observation-To‑Agent (O2A) platform observes how humans actually work – every click, keystroke, workaround and judgement call across your entire tech stack.
This digital footprint becomes a living blueprint that AI agents follow to execute complex, multi-step processes from start to finish – providing full context about variations, compliance requirements, and the inevitable exceptions that break traditional automation.
Where Today’s “Agentic” Approaches Fail
Task-first approach
Local and inflexible: Pre-scripted steps fail to adapt to small variants – they optimize a task, not the end-to-end outcome.
Low context, high exceptions: Without a versioned process model, agents lack the necessary context to make informed decisions, provide clear explanations, and implement improvements.
Database-first approach
Insight without execution: Analytics, logs, and prompt-tuning describe the work but don’t run it end-to-end.
Stale snapshots: Static datasets miss edge cases and drift from how work actually happens; exceptions bounce back to humans.
Platform-first approach
Bounded by its own walls: Great at automating what lives inside the platform, but blind where enterprise work happens: desktops, vendor portals, legacy UIs, email/attachments, and human handoffs.
Model-first approach
Consumer patterns ≠ enterprise reality: Pretrained largely on web/shopping/search flows, models lack enterprise semantics—case IDs, role entitlements, approvals, evidence chains, etc.
Short-horizon habits: Optimized for single-session tasks; enterprise cases span weeks, teams, and systems. Without observed execution telemetry and case memory, plans break, policy is missed, and explainability suffers.
What Changes with Context-Aware Agentic Execution
Context-aware agentic execution reverses this logic: first capture how work is done, then run governed agents across UI and APIs with that context. The result: automation that adapts, learns, and delivers consistent outcomes in complex environments.
The Six Principles: A New Standard for Enterprise AI
Telemetry over assumptions. Train and govern agents on observed human–system interactions (clicks, keystrokes, decisions) across approved apps – use reality, not assumptions.
Execution over analytics. Insights must drive end-to-end execution – agents plan, act, and verify outcomes across UIs and APIs – closing the loop from “knowing” to “doing.”
Transparent governance. Policy-as-code, case memory, and step-level evidence make every action explainable and reversible; approvals, audit trails, and rollback are native behaviors, not afterthought integrations.
Open architecture. Plug into existing systems, models, and controls without rip-and-replace—model-agnostic, connector-rich, and standards-friendly (MCP) so agents operate wherever the work lives.
Outcome driven metrics. Success is measured in business outcomes—not bot counts: cycle time, first-pass yield, exception rate, compliance adherence, and cost-to-serve are first-class metrics tied to each case.
Human-AI collaboration. Humans and agents work as one team: clear roles, escalation paths, and human-in-the-loop gates; expert interventions become reusable skills, compounding reliability over time.
“You cannot improve what you do not capture. When operational telemetry informs agents and controls are built in from the start, you see faster resolution times and more consistent compliance. These principles reflect what it takes to deliver durable outcomes in complex organizations.” — Cijo Joseph, Chief Technology & Digital Officer, Mitie
Why This Matters Now
Enterprise operational knowledge remains largely undocumented, residing in individual expertise and informal practices. By systematically capturing work patterns and encoding them into intelligent automation, organizations can accelerate processing, ensure regulatory compliance, and enhance customer experience – while maintaining complete control over data privacy and operational risk.
This approach transforms automation from a tool that handles simple, repetitive tasks into a capability that manages complex, variable workflows requiring judgment and context.
For organizations ready to implement context-aware automation that delivers measurable business outcomes, visit Skan AI at https://www.skan.ai
About Skan AI
Skan AI, the leader in enterprise process intelligence, provides the industry’s first living system of record for process execution. Through comprehensive capture of human-system interactions, Skan AI creates operational blueprints that enable AI agents to execute complex workflows with full context, compliance, and control. Fortune 500 enterprises deploy Skan AI to transform operations across any industry and use case, including service delivery, claims processing, underwriting, and financial operations.
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SOURCE Skan