Agentic Governance: A Practical Guide to Governing AI Agent Systems
How do you govern AI agents in production? From API governance and security to observability and lifecycle management, a …
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Build enterprise AI that actually works.
Design patterns. Protocols. Governance. Everything you need to take AI agents from prototype to production.
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Agentic Primitives: 6 Building Blocks Every AI Agent System Needs
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The Four Dimensions of Agent Autonomy
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The Applied Autonomy Framework: Bridging Agent Design and Operations
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Enterprise AI doesn't fail because of models.
It fails because of missing contracts, unclear governance, systems that were never designed for agents, and orchestration patterns borrowed from tutorials instead of production environments.
Agentic Academy is the resource that was missing — practical, opinionated, and built for people who ship.
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Choose your path
Whether you're building agents, designing agentic architectures, or leading AI strategy — we've curated a reading path for you.
Builders
Engineers building agents
You're hands-on with LLMs and agents. Start with the primitives framework, learn orchestration and error handling patterns, then tackle the gap between prototype and production.
See reading list →Architects
Designing agent systems
You're designing how agents fit into enterprise systems. Start with primitives and protocols, then master the patterns that make multi-agent systems work at scale.
See reading list →Leaders
Governance & strategy
You're responsible for AI strategy, risk, and governance. Start with understanding what agents actually are, then explore governance frameworks and the path from prototype to production.
See reading list →Featured Article
Agentic Primitives: 6 Building Blocks Every AI Agent System Needs
The six fundamental building blocks of AI agent systems: actors, tools, instructions, coordination, interactions, and governance. A practical framework for enterprise agent design.
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What we cover
Agent Fundamentals
Core concepts and architectural patterns for building AI agents. From ReAct to multi-agent systems, memory management to tool integration.
View articles →Agent Design & Frameworks
Design patterns, frameworks, and methodologies for building production-ready AI agents. From the AI Agent Canvas to testing strategies.
View articles →Protocols & Standards
The protocols and standards enabling agent-to-system and agent-to-agent communication. MCP, AsyncAPI, OpenAPI, and the emerging standards landscape.
View articles →Governance & Operations
Frameworks for governing AI agents in enterprise environments. Risk management, observability, cost control, and avoiding the false economy of speed.
View articles →Enterprise Integration
Connecting AI agents to enterprise systems, data, and workflows. API management, security, authentication, and hybrid human-agent operations.
View articles →Research
Cutting-edge research, academic papers, and scientific advances in agentic AI systems
View articles →Resources
Recommended reading
Handpicked content from voices we trust in the enterprise AI space.
API Experience Podcast
Hosts Matt McLarty and Mike Amundsen take a look across the digital landscape and identify key focus areas that are currently impacting the world of APIs, Integration, and Business.
Listen now →
Building Effective Agents
Anthropic's practical guide to building LLM agents. Explores composable patterns from simple workflows to autonomous agents — emphasizing that the most successful implementations avoid complex frameworks in favor of simple, well-understood building blocks.
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Writing Effective Tools for Agents
A practical guide to designing high-quality tools for AI agents. Covers how to build prototypes, run evaluations, and iteratively improve tools through agent collaboration — with key principles on token efficiency, tool selection, and effective descriptions.
Read blog →About
From the field, not the sidelines
Markus Müller (Global Field CTO for API Management, Boomi) and Matt McLarty (CTO, Boomi — O'Reilly author, API Experience podcast host) bring decades of enterprise architecture experience to every article.
This isn't analyst commentary from the sidelines. It's field notes from practitioners building, advising, and shipping in the enterprise AI space.
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