OpenAgents Episode 157: Chains of Thought and Action
We define a chain of thought and action (CoTA) as a series of reasoning steps and tool use whereby agentic AI systems show both the intermediate reasoning and the inputs and outputs of actions taken.
We plan a "GitHub issue solver" agent we'll build over the next few videos to show CoTA in action. A possible algorithm:
- Given a GitHub issue, build a repo map from issue (via automated script)
- Identify relevant files (via DeepSeek R1 for reasoning, Mistral Small(?) for structured output)
- Traverse and analyze codebase (file readers, AST parsers)
- Plan changes (DeepSeek R1)
- Generate and test code changes (CI/scripts)
- Create pull request with detailed explanation (GitHub API)
When this agent works well with full transparency, why would you ever use a closed-source alternative?
If you expect your models to show the full CoT, you'd better expect your agents to show the full CoTA!
More 👉 https://openagents.com/cota
Watch on X: https://x.com/OpenAgentsInc/status/1886297781138030777
