Agentic Process Automation has agents participating directly in business processes, making step-level decisions, interpreting mixed inputs, coordinating across systems, and operating within policy and control boundaries. That shift matters because many enterprise processes now depend on judgment over documents, messages, exceptions, thresholds, and domain rules that do not fit cleanly into deterministic flow logic.

When an agent executes one of those enterprise steps, output quality depends on whether it receives the right context for that specific task: the right subset of enterprise knowledge, containing the correct definitions, policy constraints, exceptions, and decision thresholds. In most enterprises, that context is fragmented across policy manuals, standard operating procedures, source systems, regulatory texts, tickets, emails, and human judgment. As a result, the knowledge needed for a single decision step is rarely assembled in a form that is complete, scoped, and usable at execution time.

The Agentic Knowledge Fabric (AKF) is the knowledge foundation for Agentic Process Automation (APA). It addresses that operating gap by converting fragmented enterprise knowledge into bounded context artifacts that can be retrieved and assembled under explicit context limits. 

AKF is built on an engineering premise: context should be treated as a product with predictable size, stable identifiers, provenance, and deterministic assembly. That premise drives both the logical architecture—how meaning is represented, indexed, linked, and selected—and the operational pipeline that builds and maintains those representations.

What if agents could do more than assist developers — what if they became trusted participants in enterprise business processes?

In this episode, we explore Agentic Process Automation (APA) and why it’s the next step beyond personal AI agents. While coding agents operate under individual credentials and user interfaces, enterprise agents must run headless, integrate across SaaS platforms, and meet strict governance, identity, and trust requirements.

We discuss:

• Personal vs. enterprise agents
  

• Why brittle integrations and hard-coded logic fall short
  

• How agents can fill the “white space” between systems like Salesforce, Workday, and ServiceNow
  

• The importance of knowledge engineering and shared business context
  

• Why “Know Your Agent” (KYA) and agent governance are essential at scale
  

Agents aren’t just automation tools — they can reason, handle ambiguity, and operate 24/7 within structured business processes. But doing that safely requires new architectural thinking.

If you're building enterprise AI systems, modernizing integrations, or exploring multi-agent ecosystems, this episode is for you.

We’re entering an era where enterprises won’t run a handful of agents, but thousands or millions of them. In this episode, we introduce Know Your Agent (KYA) and argue that managing agents must look more like managing employees than managing software. We break down why identity, access control, auditability, and trust are now urgent problems, how lessons from KYC, HR, and data governance apply to agents, and why scaling agents without these foundations is a serious risk. If agents are moving at machine speed, trust can’t be an afterthought.

Most enterprise automation fails in the same place: the brittle gap between SaaS systems.In this episode of The Agentic Mesh Podcast, John Miller and Eric Broda argue that traditional RPA and workflows can’t handle ambiguity, edge cases, or real-world complexity. Instead, they explore agentic process automation, where AI agents powered by large language models fill the “white space” between platforms like Salesforce, ServiceNow, and Workday.They break down why knowledge engineering, context, identity, and emerging standards like MCP and A2A are now mandatory for building enterprise-grade agents — not optional.If you’re still trying to glue systems together with scripts and workflows, this conversation explains why that approach is already obsolete.

Are coding agents enough, or do enterprises need a completely different class of AI to run their business processes?In this episode of The Agentic Mesh Podcast, we dive into the world of AI agents and the key differences between coding agents and enterprise agents. We explore how coding agents help developers write and iterate code, while enterprise agents tackle complex business processes, governance, and cross-team coordination.We share our experiences from the front lines—what’s working, what’s still early, and how enterprise-grade agent ecosystems are shaping the future of AI in business.Join us as we break down the evolution of coding agents, lessons for enterprises, and the exciting collision of tech and business.

How well does your AI understand your business's decision-making processes? In our latest podcast, we highlight the critical role of policies in context engineering and how they can shape AI responses effectively. Don’t let your AI miss out on this vital information! The context window is one of the most critical and constrained resources in AI. In this episode of the Agentic Mesh Podcast, we explain why context engineering—and treating policies and decision boundaries as first-class context—is essential for building reliable enterprise AI agents.We break down token limits, RAG shortcomings, and the idea of minimum viable context, using real-world examples to show how agents can operate safely, accurately, and at scale.Ideal for anyone working in agentic AI, enterprise AI, knowledge engineering, and AI governance.

In this conversation, Eric and John delve into the critical role of context in AI, particularly focusing on the concept of the 'minimum viable context' and its implications for AI agents. They discuss the challenges of managing context windows, the importance of decision traces and policies, and the need for trust in AI systems. The dialogue emphasizes the necessity of human oversight and the evolving nature of knowledge management in the context of AI.

In this conversation, Eric and John delve into the critical role of context in AI, particularly focusing on the concept of the 'minimum viable context' and its implications for AI agents. They discuss the challenges of managing context windows, the importance of decision traces and policies, and the need for trust in AI systems. The dialogue emphasizes the necessity of human oversight and the evolving nature of knowledge management in the context of AI.

The hard problem isn’t building smarter agents; rather, if we are going to have hundreds, thousands, or maybe millions of agents in each enterprise, then it’s about building the social infrastructure for trust at-scale; it’s about making the entire agent ecosystem and the agents in them transparent, auditable, certifiable – and trusted.In this episode, John Miller and Eric Broda discuss the critical aspects of trust in AI systems, emphasizing the importance of governance, compliance, and certification. They explore how trust in AI agents should be built similarly to trust in human-operated systems, focusing on the need for transparency, accountability, and a robust governance framework. The conversation also delves into the challenges of granting autonomy to AI agents and the necessity of ensuring that these systems can be audited and trusted at scale.

The future is ambient agents: always on, always listening, and always working. The hard part isn’t a prettier UI; it’s identity, state, and audit-ready agent-to-agent handoffs at scale—why A2A, MCP, and an Agentic Mesh exist.In this conversation, Eric and John explore the concept of ambient agents, which are AI agents that operate in the background, always on and listening. They discuss the implications of this new architecture, the challenges of agent communication, and the need for a shift in how we think about agent ecosystems. The conversation highlights the importance of distributed computing, security concerns, and the future of agent collaboration.