# decision models agentic ai

Published articles for decision models agentic ai.

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## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills

DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills](<https://devfeed.tech/articles/presentation-decision-models-in-agentic-architectures-from-production-to-agent-skills-17397.md>)

Original publisher: [Read original article](<https://www.infoq.com/presentations/decision-models-agentic-ai/>)

Author: Alex Porcelli

Published: 2026-09-14T11:00:00Z

Content type: article

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [NeMo](<https://devfeed.tech/topics/nemo.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agent-skills](<https://devfeed.tech/tags/agent-skills.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-architecture](<https://devfeed.tech/tags/agentic-ai-architecture.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-architecture](<https://devfeed.tech/tags/ai-architecture.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [architecture-design](<https://devfeed.tech/tags/architecture-design.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [business](<https://devfeed.tech/tags/business.md>), [decision-models-agentic-ai](<https://devfeed.tech/tags/decision-models-agentic-ai.md>), [development](<https://devfeed.tech/tags/development.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [enterprise-architecture](<https://devfeed.tech/tags/enterprise-architecture.md>), [governance](<https://devfeed.tech/tags/governance.md>), [infoq](<https://devfeed.tech/tags/infoq.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llms](<https://devfeed.tech/tags/llms.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [models](<https://devfeed.tech/tags/models.md>), [nemo](<https://devfeed.tech/tags/nemo.md>), [presentation](<https://devfeed.tech/tags/presentation.md>), [production](<https://devfeed.tech/tags/production.md>), [qcon-ai-boston-2026](<https://devfeed.tech/tags/qcon-ai-boston-2026.md>), [qcon-software-development-conference](<https://devfeed.tech/tags/qcon-software-development-conference.md>), [skills](<https://devfeed.tech/tags/skills.md>), [transcripts](<https://devfeed.tech/tags/transcripts.md>)

### AI overview

Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions.

### Source excerpt

Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli