Step-by-Step Guide to Creating Agentic MCP Flows with AI Gateway
You can make Agentic MCP Flows easily with AI Gateway and MCP Registry. These tools help you check who is allowed in. They also help you control access and watch every action. Azure API Management and Azure API Center give you one place to set rules. They help you enforce policies and keep good records. The tools are safe by default, so agents follow permission and authorization rules. You add APIs, link agents to backend services, and set rules for safe and clear flows.
Key Takeaways
Use AI Gateway and MCP Registry to make safe agentic workflows. These workflows help control who can get in and watch what happens.
Get your environment ready with cloud tools and ways to sign in. Add automation so everything works well and can grow.
Put in MCP Registry and Gateway the right way. This helps agents talk safely with backend services.
Make agent flows that are clear and easy. Give each agent a job and what they need. Test them well with live checks and expert help.
Keep things secure by letting only some people in. Write down what happens and change keys and rules often. This keeps workflows safe and follows the rules.
Setup & Prerequisites
Tools & Accounts
To begin, you need some important tools and accounts. You should have cloud platforms like Amazon EC2 or Amazon EKS. These help you deploy MCP Registry and AI Gateway. If you want to test on your computer, Docker Compose is a good choice. Make sure Docker is installed on your computer. You also need an AWS account. Set up Amazon Cognito for authentication. For managing APIs, use Azure API Management and Azure API Center.
You will use MCP servers like GitHub, Slack, Jira, or microservices. Authentication can use OAuth2, static API keys, or personal access tokens. For production, use virtual account tokens for stronger security. Group your MCP servers by environment, like development or production. This helps you set the right access controls.
Tip: Use mcp-proxy to help stdio-based MCP servers work with HTTP. This makes integration easier.
Environment Prep
Get your environment ready by setting up governance and ownership models. Decide if you want to manage MCP servers in one place or let teams do it. Choose who will handle security and maintenance. Make a service catalog to stop duplicate deployments.
Use layered security. Use OAuth 2.0 with dynamic client registration and OpenID Connect for agent authentication. Connect with your company’s identity provider, like Azure AD or Okta. This lets you use single sign-on and control access in one place. Store sensitive credentials in secure vaults. Keep detailed audit logs.
Put your MCP servers in containers using Docker. Use Kubernetes if you need to scale and keep things running. Automate your setup with tools like Terraform. Set up CI/CD pipelines to build and deploy MCP servers automatically. Watch your environment with tools like Prometheus and Grafana.
Install MCP Registry & Gateway
You can install MCP Registry and Gateway with Docker or Docker Compose. Here is a simple guide:
git clone https://github.com/docker/mcp-gateway
Go to the example directory:
cd mcp-gateway/examples/mcp_toolkit
Use Docker Compose to set up your gateway service. Set the image, ports, and volumes.
Start the services:
docker compose up -d --build
To enable an MCP server, run:
docker mcp server enable duckduckgo
Connect a client, like Visual Studio Code:
docker mcp client connect vscode
Run the MCP Gateway:
docker mcp gateway run
Now you have a containerized MCP environment. You are ready to onboard APIs and connect agents.
Configure MCP Registry & Gateway
Registry Init
You begin by setting up the MCP Registry. This step helps agents talk safely and reliably. First, run this command to make folders, set permissions, add tokens, and start the registry database:
claude --dangerously-skip-permissions
This command helps stop problems with logging in or connecting. Next, put your MCP servers in a special folder like ~/.claude-mcp-servers/. Make smaller folders for things like apis/, utilities/, and development/. This setup makes it simple to organize, protect, and save your servers.
When you add MCP servers, use the user scope (--scope user). This makes servers work everywhere in all projects and folders. Do not use local scope, because it only works in one folder. For real use, always pick user scope so you do not get "server not found" mistakes.
Tip: Use a bigger
initTimeout(like 150000 ms) to fix slow OAuth logins when starting servers.
Gateway Config
You set up the AI Gateway to keep agent flows safe and easy to watch. The gateway uses TensorZero to connect many large language model providers with one API. You get model routing, backup plans, and retry steps. Schema enforcement makes sure outputs are neat and expected.
Guardrails-AI checks inputs and outputs. It looks for unsafe stuff, prompt tricks, and tries again if results are bad. LLAMA CodeShield checks AI-made code for problems before it goes to other systems. The gateway keeps logs of guesses, retries, fails, and token use. This helps you see what happens and fix or improve your workflows.
Note: Human-in-the-loop stops let people check for safety and correctness in important workflows.
Connect Registry & Gateway
You link the MCP Registry and Gateway so agents can talk easily. First, set up login tools using Amazon Cognito. Make a Cognito User Pool, set up OAuth 2.1 with PKCE, and add resource servers and scopes. Then, make the AgentCore Gateway endpoint. This is the main spot that connects agent talks with outside APIs.
Set up API targets by making credential helpers, uploading OpenAPI specs, and setting up how to log in. Add the agent client (like Strands client) to use the AgentCore Gateway. This setup lets agents and APIs talk in a safe and clear way.
Now you have a strong, safe, and easy-to-watch base for agentic workflows.
Register Tools & Services
Register via CLI
You can use the command-line interface (CLI) to add tools and services to the MCP Registry. First, open your terminal. Then, type this command to add a new MCP server:
mcp registry add --name jira --type rest --url https://your-jira-instance/api
This command adds your Jira REST API as an MCP server. You can do the same for other services like Slack or GitHub. Give each server its own name and type. Put servers into groups like development or production. This helps you keep access rules clear. After you add a server, check its status:
mcp registry list
You will see all MCP servers and their current states. This step lets you make sure your tools are ready for agents to use.
Expose Services
Once you register your tools, you need to share them safely with the MCP Gateway. It is important to keep things secure when letting AI agents use backend services. Here are some good tips:
Check your authorization logic to stop unwanted data access.
Keep tokens safe and check them every time.
Watch for prompt injection attacks. These can make AI agents act in strange ways.
Use encryption and authentication for every endpoint you share.
Keep data and tokens for each tenant separate to stop leaks.
To share a service, run this command:
mcp gateway expose --server jira --tools createIssue,updateStatus
This command lets your agents use certain Jira actions. Always test these endpoints to make sure they are safe.
Use Case Example
Think about working at a big company. You want to connect your REST APIs to AI agents without changing them. Use Azure API Management to add your APIs as MCP servers. In the Azure portal, pick the REST API you want to share. Choose which actions to let agents use as tools. Test the setup with MCP Inspector or Visual Studio Code in agent mode. Manage all MCP servers in Azure API Center. You can track versions, set rules, and control access from one place. This way, you do not have to build things twice. You also get better security with OAuth 2.0 and can watch everything from one spot. This helps you make a safe and easy-to-grow space for your AI work.
Build Agentic MCP Flows
Design Flows
You begin by planning how agents and tools work together. Use domain-driven design to group MCP servers by business area. This makes flows simple to follow and fix. Organize your system into layers. The Domain Layer holds business logic and models. The Application Layer helps agents talk to MCP servers and change protocols. The Infrastructure Layer manages technical things like HTTP clients and data formats.
Give each agent and tool one clear job. For example, one agent creates tickets. Another agent checks ticket status. Set clear input and output for every tool and agent. This stops confusion and helps you fix problems faster.
Use the same language in all flows. Make sure humans and AI agents know the terms. Design interfaces so large language models pick the right tool for each job. Keep tasks small and separate. This helps you grow flows and add new features without breaking old ones.
Make cross-team groups to review flows. Include people from engineering, data science, and compliance. Build a shared platform for reusable parts and dashboards. Match flows with real business needs. Train teams and share knowledge so everyone understands.
Tip: Use event-driven triggers to make flows smarter. For example, set up a webhook to start a flow when a new support ticket comes in.
Test & Observe
Testing Agentic MCP Flows helps you find problems early. Start with simple, stable workflows. Use tools like Playwright or your favorite IDE to run tests. Build a proof of concept by linking an MCP server to an agent. Measure time saved and how well the agent handles changes.
Run stress tests with benchmarks like LiveMCP-101. This benchmark covers many hard tasks and shows how flows handle real challenges. Try running two agents at once. One agent follows a plan. The other agent works on its own. Compare results to find mistakes and make flows better.
Set up real-time observability with your AI Gateway. Use dashboards to watch every request between agents, MCP servers, and language models. Export logs to tools like OpenTelemetry for deeper checks. This gives instant feedback on model behavior, request flow, and speed. You can spot slowdowns, fix bugs faster, and keep flows safe.
Note: Always include domain experts in testing. They help shape agent outputs and catch rare problems.
Secure & Govern
Security and governance are very important for Agentic MCP Flows. Start by listing all agents, models, and data flows. Focus on big risks like sensitive data and compliance needs. Set strong company rules for what agents can do, who owns them, and how you manage their lifecycle.
Treat agents as dynamic identities. Use token-based authentication and just-in-time access. Manage permissions with identity control planes and OAuth 2.0/2.1 with PKCE. Watch agent behavior all the time. Get ready for new compliance rules.
Follow these steps to set guardrails. Limit API exposure and only share endpoints agents need. Use role-based tokens to control what each agent sees. Set adaptive rate limits to stop abuse. Log every action with details like context, time, and agent identity. Keep test and production environments apart. Scan all files for sensitive data before going live. Rotate keys and secrets often. Simulate agent actions in a safe place before full rollout.
Use policy-driven workflows for approvals and checks. Orchestrate multi-agent tasks with shared state and compliance checks. Support self-service for agents, but never let them skip security. Use audit trails to track every action for reports and compliance.
Alert: Protect sensitive data during testing by using data masking. Never use real production data in test environments.
You can add event-driven extensions to Agentic MCP Flows. For example, connect agents to systems like GitHub, Slack, or PagerDuty. Use webhooks to trigger flows when events happen, like a failed pipeline. Your agent can check logs, look at code changes, and create detailed issues automatically. This brings smart automation to workflows and cuts down manual work.
By following these steps, you build Agentic MCP Flows that are secure, easy to watch, and ready for enterprise use.
Extend & Troubleshoot
Add Integrations
You can make your MCP Gateway better by linking it to popular tools. The gateway works like a bridge. It lets you connect any agent framework to any model or tool. This setup is flexible, safe, and can grow with your needs. You get smart request routing, caching, batching, load balancing, easy access controls, and audit logs.
Here are some integrations you can use:
Database integration: Connect to SQL Server 2025 for safe access to organized data. Use Zero Trust and Microsoft Entra for extra safety.
Real-time data integration: Use Azure Integration Services to bring in live data. This helps agents decide fast.
API-based integration: Manage and protect your AI APIs with Azure API Management and GenAI Gateway.
You can also use frameworks like Zapier, LangChain, LlamaIndex, OpenAgents, and Pomerium. Each one has special features for automation, orchestration, and security.
Tip: Pick integrations that fit your business needs, like helping customers or making work faster.
Common Issues
Sometimes, MCP Registry and Gateway have trouble connecting. These problems can happen because of different transport protocols or missing authentication. Make sure your gateway uses the same protocol as your MCP servers. Check that your authentication credentials, like OAuth2 or header tokens, are set up and working.
Try these steps to fix problems:
Check connection logs. Look for 'Connected' messages.
List tools on MCP servers. If you see tools, network and server are fine.
If tools do not show up with your client, the problem is in the client integration layer.
Check the tool registration step. Silent failures often happen here.
Test in different environments to see if the issue is with configuration.
Note: Use observability and analytics to watch connection status and usage. This helps you find and fix problems fast.
Maintenance Tips
Keep your MCP Registry and Gateway working well by following good habits. Use versioning and diff tracking for prompts and tools. Always test updates in a staging area before using them in production. Run automated tests to catch problems early.
Use strong authentication and authorization controls. Give agents only the access they need. Clean metadata and use cryptographic signing to stop tampering. Watch runtime behavior and look for strange activity.
Use containers and sandboxing for safe execution. Deploy with Docker or Kubernetes to help scale. Use Redis for caching and federation. Manage everything with the Admin UI for real-time monitoring.
Use structured logging and health endpoints.
Set up metrics to keep track of things.
Change keys and secrets often.
Use built-in security like Basic Auth, JWT, rate limiting, and retry policies.
🛡️ Regular maintenance keeps your workflows safe, scalable, and ready for big jobs.
When you use AI Gateway and MCP Registry to make Agentic MCP Flows, you get real benefits. Companies see better use of API rate limits. There is less downtime and stronger security. You can also save money and see what is happening in your systems. Scalable clusters and detailed access controls help you grow and keep things running well.
Try these steps next:
Test custom adapters in a safe test area.
Try out multi-agent workflows and check the results.
Take part in open-source projects and give your thoughts.
For more help, look at the official guides about authentication, deployment, and best ways to do things. Check out community forums, GitHub SDKs, and tutorials to learn more and talk with others.
Use these tools in your cloud AI projects. You can help build the future of agentic workflows.
FAQ
How do you update an MCP server after registration?
To update an MCP server, use the mcp registry update command. Add the server name and new details. This keeps your tools up to date. Agents can use the latest features.
What should you do if an agent cannot connect to a backend service?
First, look at your network settings. Check your authentication tokens. Make sure the MCP Gateway and Registry use the same protocol. Look at the logs for any errors. Restart the agent if you need to.
Can you use your own authentication provider with MCP Gateway?
Yes, you can use your own provider, like Azure AD or Okta. Set up OAuth 2.0 or OpenID Connect in your system. This lets you control who gets access. You can use single sign-on for easy login.
How do you monitor agent activity in real time?
Use the AI Gateway’s dashboards to watch agent activity. These show every request, response, and error. Export logs to Grafana or OpenTelemetry for deeper checks.
What is the best way to test new agentic flows safely?
Test new flows in a staging area. Use fake data and audit logs. Ask domain experts to check the outputs. This helps you find problems before going live.