Hey everyone, I've been deep-diving into building some pretty complex AutoGen workflows lately, and I've hit a wall. My agent configurations—with all their nested instructions, LLM settings, and human_input_mode flags—are starting to look like a bowl of spaghetti. 😅
I'm trying to apply good DevOps practices here, but I'm unsure about the best approach. How are you all handling:
* **Version Control:** Are you storing the entire config as a single YAML/JSON file? Breaking it into modular pieces (like separate files for agent definitions, workflow logic, and system prompts)?
* **Testing:** What does a "test" even look like for an agent configuration? I'm thinking about:
* Unit tests for individual agent behaviors (e.g., "does this analyst agent correctly extract a task from a prompt?").
* Integration tests for multi-agent conversations (e.g., "does this planner -> coder -> reviewer loop produce a valid script?").
* How to mock the LLM calls effectively to keep tests fast and deterministic.
* **Change Management:** Rolling out a new system prompt or a tweaked temperature setting feels risky. Do you have a staging environment for your agent teams?
I'd love to hear about your setups, especially if you've integrated this into a CI/CD pipeline. Share your war stories and what tools (if any) have made your life easier!
Keep automating!
Great question. I've run into similar issues managing complex configurations, though my focus is usually on pricing models rather than agent logic. On the version control point, I'd strongly recommend against a single monolith file. The config drift becomes unmanageable.
For testing, you're on the right track. Think of it like testing a financial model - you need deterministic inputs and outputs. Mocking the LLM is non-negotiable for unit tests. I use a simple wrapper that intercepts calls and returns fixtures from JSON files based on a hash of the prompt parameters. This lets you version the expected responses alongside the configs. For integration tests, you'll need to define success criteria more rigidly than "produces a valid script" - maybe "output contains function def and no syntax error flags." It's tricky.
How are you tracking the cost implications of different configs? A temperature change or a longer system prompt can significantly alter token consumption. That's a test metric I always include.
Spreadsheets or it didn't happen.
Completely agree with user648 on avoiding the monolith config. What's worked for me is treating agent definitions like Terraform modules. I have a base module for a generic "assistant" with common parameters (LLM config, human_input_mode), then compose specific agents by passing in their system prompt and capabilities as variables.
For testing, I built a lightweight pytest fixture that swaps out the actual LLM client with a local mock. The key is to hash the system prompt + the first user message to create a deterministic fixture key. This lets you version the mock responses right next to the agent config in git.
On change management, we use a separate "staging" config directory that points to a lower-cost LLM (like gpt-3.5-turbo) and run our integration test suite against it before promoting to production. It catches most prompt regression issues without breaking the bank.
Cloud cost nerd. No, I don't use Reserved Instances.