Tag: PydanticAI
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Cost-Aware Agentic Workflows with PydanticAI
Introduction: The Hidden Price of Autonomy The Architecture of a Cost Guardrail Implementing Usage Limits with PydanticAI PydanticAI provides the primary library-level enforcement mechanism through its UsageLimits class. Real-Time Cost Tracking with LiteLLM While PydanticAI manages counts, LiteLLM converts those counts to dollars. Detailed HITL Workflow: The Slack Intervention For a SMB, a simple notification…
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The LLM-as-a-Compiler Pattern for High-Precision EDI Pipelines
As we look toward the next phase of industrial AI, the German Mittelstand is poised to move beyond “AI as a Chatbot” and toward the LLM-as-a-Compiler pattern. This represents a fundamental shift from “AI as a Librarian” to a “Deterministic Data Engineer.” The following architecture serves as a primary example of how this compiler pattern…
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Building Production-Grade Agentic RAG: A Technical Deep Dive – Part 1
Beyond Fixed Windows — Agentic & ML-Based Chunking Introduction: The RAG Gap The promise of Retrieval-Augmented Generation (RAG) is compelling: ground large language models in enterprise data, reduce hallucinations, enable real-time knowledge updates. But in practice, most RAG systems fail silently. They fail not because embedding models are weak or vector databases are slow, but…
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Designing Production-Grade GenAI Automation
A dbt Ops Agent Case Study A small, well-instrumented workflow can turn dbt failures into reviewable Git changes by combining deterministic parsing, constrained LLM tooling, and VCS-native delivery — while preserving governance through traces, guardrails, and CI. This is a blueprint to build a first Production-Grade GenAI Agent. You can find the complete implementation and…