As AI agents grow in complexity, the need for interoperable, scalable, and modular communication protocols becomes essential. The Agent Communication Protocol (ACP), a Linux Foundation-backed standard, provides a robust framework for how AI agents, humans, and tools communicate over HTTP. When paired with a cloud-native platform like IBM Cloud Code Engine, ACP enables seamless and scalable deployment of intelligent, multi-agent systems.

In this example, we explore how ACP and IBM Cloud Code Engine work together to unlock the potential of scalable, multi-agent AI applications. ACP streamlines communication between AI agents, making it easy to orchestrate complex workflows. Whether you're building chatbots, automation tools, or sophisticated agent-based applications, this combination simplifies both development and deployment.

As a practical example, we highlight a travel agency system that coordinates with specialized agents—such as those for city recommendations, hotel search, flight planning, and budget management. The ACP framework ensures each agent communicates effectively, while IBM Cloud Code Engine handles the deployment of the entire system, including the front-end interface and backend services. With just a few commands, developers can launch a fully operational application without worrying about infrastructure.

Ultimately, the real power of ACP and IBM Cloud Code Engine lies in their general applicability. From intelligent assistants to enterprise automation, this stack provides a scalable, modular, and developer-friendly foundation for the future of agent-based AI systems.

The system is built around six specialized agents, plus a concierge agent that orchestrates the flow:

• City Selection Agent – Picks the best destination cities based on preferences.
• City Expert Agent – Fetches local insights: attractions, restaurants, hidden gems.
• Budget Planner Agent – Ensures the plan stays within the traveler’s budget.
• Flight Planner Agent – Suggests and organizes flight options.
• Hotel Planner Agent – Recommends accommodations.
• Itinerary Planner Agent – Combines everything into a day-by-day schedule.
• Travel Concierge Agent – Acts as the conductor, calling each agent in sequence and compiling the final result.

The Travel Concierge Agent coordinates all agents using sequential chaining:

• Receives user input (destination type, budget, duration, preferences).
• Calls the City Selection Agent to determine where to go.
• Passes the city choice to the City Expert, Flight Planner, Hotel Planner, and Budget Planner agents in sequence.
• Finally, sends all results to the Itinerary Planner Agent to build the final day-by-day travel plan.
• Returns the full plan to the user.

You can deploy the agents locally or on Code Engine

Download image with AMD64 architecture

podman pull --platform linux/amd64 registry.access.redhat.com/ubi9/python-312:9.6-1752571600

Verify architecture

podman run --platform linux/amd64 -it registry.access.redhat.com/ubi9/python-312:9.6-1752571600 arch

Install:

curl -fsSL https://ollama.com/install.sh | sh

OLLAMA_HOST=$(ipconfig getifaddr en0):11434 ollama serve

ollama pull qwen3:8b

Install:

brew install podman
brew install podman-compose

vim .env

What's my machine ip?

ipconfig getifaddr en0

INFERENCE_BASE_URL=http://<YOUR-MACHINE-IP>:11434/v1
INFERENCE_API_KEY=<YOUR-API-KEY>
INFERENCE_MODEL_NAME=openai/qwen3:8b

INFERENCE_BASE_URL=https://us-south.ml.cloud.ibm.com
INFERENCE_API_KEY=<YOUR-API-KEY>
INFERENCE_MODEL_NAME=watsonx/meta-llama/llama-3-3-70b-instruct
INFERENCE_PROJECT_ID=<YOUR-PROJECT-ID>

make start-podman

podman ps

open http://localhost:5555

Use ACP client example

uv run clients/travel-concierge-agent.py

Unspecific request

Plan a trip from Stuttgart to Oslo

Detailed request with one destinations

Plan a trip from Stuttgart to Oslo from October 10 for 6 days. I'm interested in sightseeing, coffee, family hotel, restaurants, activities with kids and technology trends. My budget is Mid-range (€1000-€1800) and I prefer historical sites, local cuisine, and avoiding crowded tourist traps

Detailed request with multiple possible destinations

Plan a trip from Stuttgart to either Paris or Rome from October 10 for 6 days. I'm interested in sightseeing, coffee, family hotel, restaurants, activities with kids and technology trends. My budget is Mid-range (€1000-€1800) and I prefer historical sites, local cuisine, and avoiding crowded tourist traps

make stop-podman

Let's deploy the ACP agents on IBM Cloud Code Engine. You need to Login into your IBM Cloud account using the IBM Cloud CLI.

As the inferencing service you either need a watsonx.ai project or an Open AI api-key:

Based on your choice, create the .env file with the following variables:

INFERENCE_BASE_URL=https://us-south.ml.cloud.ibm.com
INFERENCE_API_KEY=<YOUR-API-KEY>
INFERENCE_MODEL_NAME=watsonx/meta-llama/llama-3-3-70b-instruct
INFERENCE_PROJECT_ID=<YOUR-PROJECT-ID>

# Alternative for Open AI
#INFERENCE_BASE_URL=https://api.openai.com/v1
#INFERENCE_API_KEY=<YOUR-API-KEY>
#INFERENCE_MODEL_NAME=openai/gpt-4o
#INFERENCE_PROJECT_ID=""

Login to the target region

REGION=eu-es
ibmcloud login -r $REGION --sso

Run the following command to create a Code Engine project and deploy the agents as Code Engine applications

make deploy-codeengine

The script will create a new Code Engine project in the selected region and deploy the the agent and chat applications. You can see the applications being deployed in the Code Engine User Interface

Use the following command to determine the chat applications URL

ibmcloud ce app get --name travel-concierge-chat -o json | jq -r '.status.url'

Open the URL in the browser and start chatting with the Travel Concierge agent

Run the following command to teardown the deployment and delete the Code Engine project.

make undeploy-codeengine