Tag: PydanticAI
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Deterministic Control Over Non-Deterministic AI: Why Production Agents Demand PydanticAI and LangGraph
In production services relying on raw string interpolation, unhandled execution faults stem directly from four output failure modes: dropped mandatory payload keys, string-to-float coercion failures, malformed JSON due to unescaped control characters, and hallucinated object keys. Parsing unstructured model outputs with standard string loaders guarantees runtime crashes downstream. Large language models (LLMs) operate as probabilistic…
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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…