🛡️ Operation Check-In™

🔍 Product Overview

Operation Check-In™ offers a controlled, scalable testbed for AI agents tasked with managing structured workflows such as hotel reservation systems. This framework simulates real-world operational constraints and system prompts, ensuring agents can accurately interpret tasks, use available tools, and comply with domain-specific rulesets.

With a focus on compliance assurance, behavioral analysis, and intelligent system integration, this product empowers AI teams to rigorously vet agent responses before deployment — mitigating risk and accelerating development.

🎯 Key Capabilities

• Policy-Constrained Testing: Agents are evaluated against real-world business logic (e.g., time-based restrictions, service availability).
• Modular Scenario Design: Rapid scenario construction enables domain teams to scale test coverage as business rules evolve.
• Protocol-Compatible: Seamless alignment with MCP-based systems ensures interoperable agent-tool communication.
• Dual Runtime Support: Optimized for both containerized environments (Docker) and high-speed local testing (uv).
• Audit-Ready Validation: Performance traces and decision paths are validated via rule-based scripts for transparent reporting.

🧩 Architecture Overview

• Agent Interface Module: Connects your AI agent to a controlled MCP tool environment.
• Simulated Reservation Engine: A secure, protocol-compliant back-end used for triggering scenarios across all CRUD operations.
• Scenario Repository: A growing library of prebuilt tests that reflect real-world usage patterns and edge cases.

🛠️ Built With

• Python 3.12+ for model interface logic and validation.
• Claude AI for high-context natural language processing.
• FastMCP as a high-performance backend for tool orchestration.
• Docker & uv for versatile testing across dev and CI environments.
• GitHub Actions for CI/CD validation and artifact delivery.

🧪 Evaluation Methodology

• Business rule adherence (e.g., service exclusions, time-based limitations).
• Command execution accuracy.
• Tool usage consistency and error handling.

Each test is built to provide a fair but firm challenge — designed to surface both strengths and improvement areas in agent design.

💼 Who It's For

• AI Development Teams building autonomous agents with structured task workflows.
• Product Leaders launching AI-first user flows in complex domains.
• QA/Test Automation Engineers seeking comprehensive coverage of agent reasoning and tool execution.

📦 What You Get

• A production-grade testing system deployable in secure development environments.
• An extensible architecture with support for scenario authoring and tool integration.
• A high-fidelity protocol environment (MCP) designed to simulate operational conditions.
• Documentation, onboarding support, and integration best practices for internal teams.

🧭 Deployment Options

• Docker-First Testing for environment replication across teams.
• uv-Powered Local Runs for high-speed iterative development.
• Compatible with internal CI pipelines (e.g., GitHub Actions, Jenkins, GitLab CI).

✅ Compliance Scenarios (Examples)

• Date/Time Restrictions (e.g., blackout periods such as January).
• Data Integrity & Consistency (e.g., ID-based lookups, price validation).
• Service Composition Logic (e.g., add/remove services with conditional rules).
• Edge Case Handling (e.g., missing fields, malformed prompts, retries).

🗂️ License & Access

Operation Check-In™ is a commercial-grade solution. To access the full product suite, integration documentation, and scenario authoring toolkit, please request access or licensing or contact below.