Enforcing Governance in AI Agents: Lessons from Building a Second Brain
The article discusses the challenges of building a deterministic AI agent, using the author's experience with the MemSpren project as an example. It highlights the friction points encountered during setup and the core issue of non-deterministic behavior, where the agent fails to consistently follow documented protocols.
Why it matters
Ensuring the deterministic and reliable behavior of AI agents is crucial for building trustworthy and robust systems. The insights from this article can inform the development of effective governance strategies for AI workflows.
Key Points
- 1The agent sometimes skips protocol steps that are clearly documented
- 2The author has tried various governance approaches like cron-based synchronization, redundant state comparison, and end-of-day commit review
- 3These approaches help manage the consequences of non-determinism but do not eliminate the underlying issue
- 4The author notes that this is a common problem in AI workflows, and shares insights from Intercom's approach of strict governance layers to enforce correct agent behavior
Details
The article describes the author's experience in building an agentic second brain, MemSpren, which runs on Obsidian. During the process of integrating a Telegram bot interface, the author encountered various setup friction points, such as missing dependencies, command naming mismatches, and undocumented flags. However, the core issue that the author has been trying to solve is the non-deterministic behavior of the agent, where it fails to consistently follow the documented protocols for vault writes and other actions. The author has implemented various governance approaches, such as cron-based synchronization, redundant state comparison, and end-of-day commit review, to manage the consequences of this non-determinism. However, these methods are reactive and do not eliminate the underlying issue. The author notes that this problem is not unique to the MemSpren project and shares insights from Intercom's approach of using strict governance layers to enforce correct agent behavior, rather than relying on the model to follow instructions correctly.
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