A comprehensive step-by-step guide to building a four-agent AI pipeline in Python that turns raw text files (newsletters, meeting notes) into a prioritized daily digest. Each agent handles one task: ingesting files, summarizing with an LLM (OpenAI or local Ollama), scoring by urgency keywords, and formatting to Markdown. Every agent is containerized with Docker and orchestrated sequentially via Docker Compose using `depends_on: condition: service_completed_successfully`. The guide covers project structure, Dockerfiles, secrets management with `.env` files, retry/backoff logic for API calls, unit and integration testing, JSON logging, cost estimates, security hardening, local LLM alternatives, cron scheduling, and production deployment options including Docker Swarm, Kubernetes, and major cloud platforms.
Table of contents
Table of ContentsWhat is a Multi-Agent System (and Why Build One)?What is Docker (and Why Does It Matter Here)?How to Plan the ArchitecturePrerequisites and Environment SetupHow to Set Up the Project StructureHow to Build Each Agent Step by StepHow to Handle Secrets and API KeysHow to Orchestrate Everything with Docker ComposeHow to Test the PipelineHow to Add Logging and ObservabilityCost, Rate Limits, and Graceful DegradationSecurity and Privacy ConsiderationsHow to Use a Local LLM for Full Privacy (Ollama)Example Seed Data and Expected OutputHow to Automate Daily ExecutionTroubleshooting Common ErrorsProduction Deployment OptionsConclusion and Next StepsSort: