Enhancing Enterprise AI with Contract-First Agentic Decision Systems Using PydanticAI

In the evolving landscape of artificial intelligence, how can organizations ensure that autonomous agents make decisions that balance innovation with rigorous compliance and risk management? A recent tutorial explores this challenge by demonstrating the development of contract-first agentic systems using PydanticAI, a framework that enforces structured outputs to create reliable, policy-adherent AI for enterprise environments.

Building Reliable Agentic Decision Systems for Enterprise Use

Agentic AI systems, which enable models to act autonomously in decision-making processes, hold significant potential for enterprise applications such as risk assessment and policy enforcement. However, their deployment requires mechanisms to prevent inconsistencies or non-compliance, particularly in high-stakes scenarios involving data privacy and operational security. PydanticAI addresses this by integrating Pydantic’s schema validation with AI agents, treating output structures as enforceable contracts rather than flexible formats.

This approach shifts from prompt-based experimentation to a governed workflow, where agents must adhere to predefined business logic, including risk evaluation, confidence calibration, and actionable recommendations. The tutorial outlines a step-by-step implementation using Python, leveraging OpenAI’s models for reasoning while embedding constraints directly into the system’s architecture. By doing so, it highlights how such systems can mitigate common pitfalls in agentic AI, such as illogical outputs or overlooked regulatory requirements, fostering greater trust in AI-driven decisions.

Defining Structured Contracts for Decision Outputs

At the core of this methodology is the creation of Pydantic models that define the agent’s output schema. These models encode essential elements of enterprise decision-making, ensuring outputs are not only structured but also logically coherent. Key components include:

• Decision Categories: Outputs are restricted to “approve,” “approve_with_conditions,” or “reject,” preventing ambiguous recommendations.
• Risk Assessment: A list of at least two RiskItem objects, each detailing a risk description (minimum 8 characters), severity level (“low,” “medium,” or “high”), and mitigation strategy (minimum 12 characters). This enforces comprehensive risk identification.
• Compliance and Confidence Metrics: A boolean for compliance status and a float confidence score (0.0 to 1.0), with validators ensuring alignment—for instance, confidence cannot exceed 0.70 if high-severity risks are present.
• Actionable Elements: Lists for conditions (required for conditional approvals, at least two items) and next steps (minimum three items), alongside a rationale exceeding 80 characters and an automatic timestamp.

Implementing Validators and Self-Correction Mechanisms

Beyond schema definition, the tutorial emphasizes post-generation validators and retry mechanisms to enforce quality and policy adherence. These act as checkpoints, triggering self-correction if outputs fail validation, which enhances the system’s robustness for real-world use. Output validators include:

• Risk Quality Enforcement: Ensures at least two risks are identified, with one classified as “medium” or “high,” avoiding superficial analyses.
• Policy Control Checks: Scans the rationale, next steps, and conditions for references to security measures like “encryption,” “audit,” “logging,” “access control,” or “key management.” Non-compliant outputs are rejected, prompting retries.