---
source: hn
url: https://thenewstack.io/risk-mitigation-agentic-ai/
published_at: '2026-03-12T22:58:27'
authors:
- chhum
topics:
- agentic-ai
- api-mocking
- contract-testing
- enterprise-risk
- ai-agents
- mcp
relevance_score: 0.89
run_id: materialize-outputs
language_code: en
---

# Before you let AI agents loose, you'd better know what they're capable of

## Summary
This article discusses why enterprises must first use **contract testing, sandboxes, and high-fidelity mocks** to understand and constrain the behavior of AI agents with action-taking capabilities before deploying them. The core argument is that for agentic AI, a system’s true capabilities should be defined by its observable, testable behavior, not merely by design intent.

## Problem
- Enterprise-grade agents do not just generate text; they also **browse web pages, call APIs, execute code, and modify files**, so early mistakes can cascade and amplify, making timely auditing and accountability difficult.
- These systems are exposed to risks such as **prompt injection, security overreach, data leakage, and irreversible actions**; without mature practices, putting them directly into production is very costly.
- If an enterprise lacks a clear API catalog, sandbox, and shared mocks, teams will struggle to know “what the system can actually do,” and it becomes even harder to safely evaluate the true boundaries of an agent’s capabilities.

## Approach
- The central method is to treat **testing and mocking as foundational infrastructure for agent risk mitigation**: first observe and shape behavior in a sandbox, then gradually move closer to production.
- Adopt a **contract-first** approach: use formal specifications such as OpenAPI as the primary artifact, automatically generate mock endpoints, and supplement high-fidelity scenarios with sample data, recorded traffic, or YAML.
- Use **contract testing** so API providers and consumers share the same behavioral expectations, ensuring that mocks remain structurally consistent with real services and reducing the false sense of security caused by “fake mocks.”
- With open-source tools such as Microcks, OpenAPI, and Bruno, enterprises can build a unified “sandbox as a service” across multi-protocol environments including REST, gRPC, GraphQL, Kafka, and MQTT.
- Newly added MCP interfaces also allow mock APIs to be used directly as **LLM/agent tools**, making it possible to test how agents invoke enterprise capabilities before production.

## Results
- At BNP Paribas, within the French retail banking division, **32 squads and 500+ developers and testers** use Microcks, and the platform handles **2.5 million+ API calls per week**.
- According to public case studies, with mainframe API mocking, BNP’s **development and testing cycle was reduced by two-thirds (about 66%)**, while also reducing direct access to expensive core mainframes.
- Microcks’ lightweight local binary has a startup time of **under 200 milliseconds** and supports Testcontainers integration for Java, Node, Go, and .NET, helping narrow the “works on my laptop” gap.
- Large adopters (such as Amadeus) are described as having achieved **significant improvements in development speed**, but the article does not provide more detailed comparable baseline figures.
- For agentic AI itself, the article **does not provide standard benchmark data or model performance metrics**; its strongest concrete claim is that high-fidelity mocks + contract testing + shared sandboxes can expose and constrain agent behavior more safely before deployment.

## Link
- [https://thenewstack.io/risk-mitigation-agentic-ai/](https://thenewstack.io/risk-mitigation-agentic-ai/)