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World Congress 2025 • Aug 20, 2025 • Session details

Compose the Future: Building Agentic Applications, Made Simple with Docker

Jim Clark , Mark Cavage , Tushar Jain , Yunong Xiao

Building agentic AI applications is now as simple as running standard containers. See how Docker Compose integrates models as top-level primitives to streamline your multi-LLM architectures.

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#1 about 4 min

Bridging application containerization and modern AI agent development

The historical shift of containerizing complex environments mirrors the current need to simplify scalable AI agent development.

#2 about 3 min

Defining core characteristics of autonomous AI agent systems

True agents autonomously manage control loops, connect securely to private data, and execute complex goals rather than basic tasks.

#3 about 3 min

Navigating multi-model infrastructures and compound agent swarms

Future agent workflows require mixing foundation models with specialized local variants to balance latency, privacy, and reasoning costs.

#4 about 4 min

Standardizing AI model and tool execution using containers

Packaging models as standard OCI artifacts enables frictionless discovery and execution of open-source resources directly within existing workflows.

#5 about 3 min

Centralizing tool access and observability with MCP Gateway

Utilizing a proxy architecture allows developers to scope server responses, filter tool access, and improve overall agent efficiency.

#6 about 3 min

Declaring full agent stacks structurally with Docker Compose

Treating models and tools as native declarative resources streamlines the composition and instantiation of complex multi-agent architectures.

#7 about 4 min

Architecting cooperating local agents for an ecommerce application

Combining distinct specialized task agents allows developers to parse customer sentiment and process database queries against locally hosted large language models.

#8 about 6 min

Bootstrapping a multi-agent application with generative code assistants

Utilizing inline coding prompt generation securely maps service dependencies and bootstraps execution models for immediate local testing.

#9 about 8 min

Offloading heavy inference workloads automatically to cloud GPUs

Shifting local execution to managed remote GPUs accelerates local development loops without requiring complex network configuration or data migration.

#10 about 3 min

Translating compose stacks natively into serverless cloud environments

Leveraging serverless compatibility ensures identical configuration schemas seamlessly deploy containerized agents as on-demand production services.

#11 about 8 min

Testing high concurrency loads against a serverless agent deployment

Provisioning distributed infrastructure on the fly ensures compound agent chains continuously respond reliably under thousands of concurrent application queries.

#12 about 4 min

Maximizing cloud native capabilities for scaling dynamic AI workloads

Serverless container compute natively supports stateful volumes, auto-scaling instances, and rapid GPU provisioning times for fast application serving.

#13 about 5 min

Automating vulnerability remediation and container builds using AI agents

Deploying dedicated autonomous workspace agents analyzes exposed vulnerabilities and continuously refactors pipeline code to optimize overall image security.

Matching moments

2:59 min

Adopting AI tools for developer container workflows

Ali Alp Ali Alp · WWC Europe 2026

2:22 min

Orchestrating local developer environments with AI tools

Angie Jones Angie Jones · WWC 2025

2:31 min

Integrating generative AI into cloud-native applications

Cedric Clyburn Cedric Clyburn · WWC 2024

1:14 min

Leveraging agentic capabilities and containerized developer environments

YK Sugi YK Sugi · WWC 2025

2:37 min

Shifting to containerized AI deployment environments

Sebastian Rhode Sebastian Rhode · WWC 2024

3:31 min

Practical learnings from deploying containerized AI solutions

Sebastian Rhode Sebastian Rhode · WWC 2024

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