Brains vs Hands: How to Run AI Agents Safely in Production — Viren Baraiya
Running AI agents in production requires a fundamental architectural shift: the agent's role must be separated from the execution mechanism. The speaker emphasizes that the LLM (the 'brain') should only be responsible for planning the next steps, while a deterministic, durable workflow engine (the 'hands' or harness) must handle the actual execution. This approach treats agent harnesses as 'late-bound sagas,' ensuring reliability, managing side effects, and guaranteeing deterministic outcomes, which is crucial for mission-critical systems like SRE or payment processing.
Key takeaways
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Agent Scope Beyond Chatbots
0:01
In production, agents are not limited to simple chatbots. They must handle background tasks, run on schedules, react to events (e.g., logs, alerts), and coordinate in multi-agent systems (1:52, 2:27).
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Harness as the Application
0:03
An agent, when combined with a controlling harness, functions as an application, much like a set of microservices. The harness is responsible for delivering the overall business goal, integrating databases, internal systems, and human-in-the-loop approvals (3:02, 3:12).
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The Brain and the Hands Separation
0:09
The core principle is the clear split: the LLM plans what should happen next (non-deterministic), but the harness executes the plan using deterministic code. This ensures reliable execution, especially for critical tasks like cluster restarts (9:56).
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Agentic Workflows as Late-Bound Sagas
0:10
Agent harnesses are described as 'late-bound sagas.' They gain the benefits of traditional sagas (visibility, control) while allowing the agent to propose and build the workflow at runtime, rather than requiring the entire sequence to be defined upfront (10:21).