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Observer Agents (Roadmap)

Observer Agents are specialized agents designed to monitor and modulate the execution of other agents running their own set of playbooks. These observer agents assess the correctness of each executed step, evaluate longer time horizon objectives, and proactively influence the observed agent to ensure proper execution.

Overview

In Playbooks AI, one agent can continuously observe another agent’s execution after each statement or step. The observing agent evaluates whether the observed agent is executing correctly based on predefined evaluation criteria. If the observer detects any deviation, it actively intervenes to rectify the erroneous behavior. Additionally, observer agents can evaluate longer-term objectives and goals, ensuring the observed agent's execution aligns with strategic metrics over extended periods.

How Observer Agents Work

Evaluation After Each Step

One of the crucial mechanisms that enable observer agents to operate effectively is the separation of LLM calls and side effects:

  • Each LLM call outputs a series of steps without directly causing any side effects.
  • Side effects occur only when the playbooks interpreter explicitly executes these LLM-generated directives.
  • Observer agents can thus evaluate each step immediately after it is generated by the LLM and before any side effects are applied.
  • If the observer finds any issues, the interpreter halts the execution of that step and incorporates the observer's feedback to guide a corrective response from the LLM.

Evaluation of Longer Time Horizon Objectives

Observer agents are not limited to immediate step-by-step assessments. They can also evaluate broader, strategic objectives over extended timeframes. Observer agents come equipped with default playbooks that dictate their behavior during each invocation. These default playbooks can be customized to implement specific evaluation criteria and metrics tailored to the agent's longer-term goals and objectives.

Distilled Observer Behavior

To ensure practical execution times and minimal overhead, the behavior of observer agents can be distilled into a small, efficient LLM:

  • This specialized LLM is tuned to produce minimal output—often just a few tokens or none at all—when execution is correct.
  • Only when the observer detects an issue will it generate explicit observational tokens indicating the detected problem.
  • This targeted distillation ensures real-time, step-by-step evaluation remains practical and performant.

Example Workflow

Consider the following scenario:

  1. Agent A executes a series of steps generated by an LLM.
  2. Observer Agent B evaluates each step immediately after generation but before side effects execution.
  3. Observer Agent B also periodically evaluates whether Agent A is meeting longer-term strategic objectives.

  4. If Observer Agent B identifies an issue:

  5. The runtime pauses the execution.

  6. The issue is noted, and corrective instructions are generated.
  7. A corrected step is requested from the LLM.
  8. Corrective steps are then executed seamlessly.

Roadmap

This Observer Agents functionality is on the development roadmap but is not yet implemented.

Benefits

  • Enhanced Reliability: Immediate detection and rectification of errors.
  • Real-time Correction: Prevents cascading issues by addressing problems early.
  • Strategic Alignment: Ensures agent actions consistently align with long-term objectives.
  • Efficient Monitoring: Minimal performance overhead due to distillation and optimized LLM usage.

Observer Agents provide a powerful tool for ensuring robust, reliable, and verifiable execution of complex agent workflows.