Stop Building AI Pipelines Start Building Model Native Agents

Описание: The era of the "simple chatbot" is dead. If you’re still forcing LLMs into rigid if/else loops, you aren’t building an agent—you’re building a prototype.

In this deep dive, we explore the transition from traditional orchestration to Model-Native Agent Systems. We break down why hard-coded pipelines fail in the face of real-world entropy and how to treat the LLM as the "kernel" of your entire system rather than just a component.

Using the Mastery Framework by Celoris Designs, we’ll walk through the architectural shift required to build production-grade autonomous systems that reason, navigate latent space, and self-heal.

🔍 What You’ll Learn:
The Pipeline Trap: Why external logic is the biggest bottleneck in agentic scaling.

The Agentic Stack: A breakdown of LangGraph, PydanticAI, and the Model Context Protocol (MCP).

Baking Reasoning: How SFT and RLHF are moving logic from prompts into model weights.

Real-World Applications: Autonomous researchers, self-healing DevOps agents, and Latent Memory systems.

🛠️ Featured Tools & Frameworks:
Orchestration: LangGraph, PydanticAI

Connectivity: Model Context Protocol (MCP)

Infrastructure: Modal, Lambda Labs

Observability: LangSmith, Weights & Biases

Graph Memory: Neo4j

🎓 Master the Kernel
Ready to level up? Celoris Designs offers an intensive 8-week program designed for advanced Python developers to master the Model-Native architecture. Get $500 in compute credits and access to our private MCP hub.

🔗 Join the Mastery Framework:
https://celorisdesigns.com/courses/bu...

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