Checklist
Checklist
Checklist
Get Agent Ready Checklist
What you need to check off as you go through your AI Agent journey
Section 1: Understand the MCP Ecosystem
Grasp what MCP really is and how it’s changing integration.
0/5
Identify how your product could act as an MCP server or client
Map the role of your data and APIs in an agent-driven workflow
Review OpenAI’s MCP documentation and examples
Evaluate compatibility with your existing APIs, SDKs, or plugins
Define what success looks like for MCP adoption in your org
0/5
Identify how your product could act as an MCP server or client
Map the role of your data and APIs in an agent-driven workflow
Review OpenAI’s MCP documentation and examples
Evaluate compatibility with your existing APIs, SDKs, or plugins
Define what success looks like for MCP adoption in your org
0/5
Identify how your product could act as an MCP server or client
Map the role of your data and APIs in an agent-driven workflow
Review OpenAI’s MCP documentation and examples
Evaluate compatibility with your existing APIs, SDKs, or plugins
Define what success looks like for MCP adoption in your org
Goal: Build awareness of the MCP standard and its impact on your business model.
Section 2: Audit Your Existing Architecture
Assess technical readiness for MCP integration.
0/5
Review your current authentication and session handling setup
Identify which APIs could safely expose capabilities via MCP
Map data flows between internal systems, third-party tools, and users
Document any dependencies that would block real-time agent interactions
Evaluate your infrastructure scalability and latency tolerance
0/5
Review your current authentication and session handling setup
Identify which APIs could safely expose capabilities via MCP
Map data flows between internal systems, third-party tools, and users
Document any dependencies that would block real-time agent interactions
Evaluate your infrastructure scalability and latency tolerance
0/5
Review your current authentication and session handling setup
Identify which APIs could safely expose capabilities via MCP
Map data flows between internal systems, third-party tools, and users
Document any dependencies that would block real-time agent interactions
Evaluate your infrastructure scalability and latency tolerance
Goal: Create a baseline assessment to know what needs upgrading before MCP.
Section 3: Secure Access and Identity
Ensure every human, agent, and API call is authenticated and auditable.
0/5
Review identity models for both human and non-human (agent) users
Implement scoped credentials and tokens for MCP sessions
Plan for API key rotation and credential hygiene
Add audit logging for agent actions and context requests
Evaluate integration options with Prefactor or equivalent identity layers
0/5
Review identity models for both human and non-human (agent) users
Implement scoped credentials and tokens for MCP sessions
Plan for API key rotation and credential hygiene
Add audit logging for agent actions and context requests
Evaluate integration options with Prefactor or equivalent identity layers
0/5
Review identity models for both human and non-human (agent) users
Implement scoped credentials and tokens for MCP sessions
Plan for API key rotation and credential hygiene
Add audit logging for agent actions and context requests
Evaluate integration options with Prefactor or equivalent identity layers
Goal: Establish a security model that prevents agent misuse and ensures traceability.
Section 4: Prepare Your Data and Permissions Model
Structure your data and policies for selective agent access.
0/5
Identify what data agents should be able to read or modify
Build RBAC or ABAC layers that align with your internal policies
Tag sensitive data and determine redaction or masking rules
Ensure permissions can adapt dynamically as agent roles evolve
Simulate agent queries to test least-privilege enforcement
0/5
Identify what data agents should be able to read or modify
Build RBAC or ABAC layers that align with your internal policies
Tag sensitive data and determine redaction or masking rules
Ensure permissions can adapt dynamically as agent roles evolve
Simulate agent queries to test least-privilege enforcement
0/5
Identify what data agents should be able to read or modify
Build RBAC or ABAC layers that align with your internal policies
Tag sensitive data and determine redaction or masking rules
Ensure permissions can adapt dynamically as agent roles evolve
Simulate agent queries to test least-privilege enforcement
Goal: Create a data access model designed for automated Agents — not just humans.
Section 5: Build Your MCP Server and Endpoints
Turn readiness into execution.
0/5
Stand up an MCP-compatible server endpoint with a test schema
Implement metadata discovery so agents can understand your resources
Add structured responses with clear types and error handling
Validate your implementation using sample MCP clients
Document supported capabilities for developers and partners
0/5
Stand up an MCP-compatible server endpoint with a test schema
Implement metadata discovery so agents can understand your resources
Add structured responses with clear types and error handling
Validate your implementation using sample MCP clients
Document supported capabilities for developers and partners
0/5
Stand up an MCP-compatible server endpoint with a test schema
Implement metadata discovery so agents can understand your resources
Add structured responses with clear types and error handling
Validate your implementation using sample MCP clients
Document supported capabilities for developers and partners
Goal: Launch a compliant MCP prototype that can be safely tested with real agents.
Section 6: Test, Monitor, and Iterate
Move from proof-of-concept to production-grade.
0/5
Simulate real-world agent requests (OpenAI, Anthropic, etc.)
Log and analyze agent behaviors for anomalies or misuse
Create test harnesses for edge cases and performance load
Define ongoing monitoring and alerting strategies
Collect feedback from both internal users and agent partners
0/5
Simulate real-world agent requests (OpenAI, Anthropic, etc.)
Log and analyze agent behaviors for anomalies or misuse
Create test harnesses for edge cases and performance load
Define ongoing monitoring and alerting strategies
Collect feedback from both internal users and agent partners
0/5
Simulate real-world agent requests (OpenAI, Anthropic, etc.)
Log and analyze agent behaviors for anomalies or misuse
Create test harnesses for edge cases and performance load
Define ongoing monitoring and alerting strategies
Collect feedback from both internal users and agent partners
Goal: Evolve from POC to a stable, secure, production-ready MCP implementation
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Copyright © 2025. All rights reserved.
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Copyright © 2025. All rights reserved.
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Copyright © 2025. All rights reserved.
Created by
Copyright © 2025. All rights reserved.
Created by