From: aidotengineer
The Challenge of Stable AI Agents
AI agents, chatbots, and workflows built on Large Language Models (LLMs) are considered capable of solving complex knowledge work problems if they can achieve stability [00:39:00]. However, these agent swarms often lack stability when tackling increasingly complex tasks [00:51:00]. This instability stems from:
- Difficulty in observing agent behavior perfectly [01:00:00].
- Challenges in testing agents comprehensively in dynamic environments [01:03:00].
- Uncertainty about whether agents are consistently progressing towards their goals [01:12:00].
Limitations of Traditional Evaluations
Simply adding an evaluation (eval) stack without systematic use is often insufficient to improve agents and workflows [01:45:00]. Effective evaluation requires continuous development and alignment with business requirements [02:05:00].
Complexity of the Evaluation Landscape
Evaluating all aspects of agent behaviors and internal representations presents a highly complex landscape [02:25:00]. A comprehensive evaluation framework encompasses:
- Semantic aspects: How the agent represents, models, discusses, and grounds reality (what is true) [02:48:00]. This relates to semantic evaluation.
- Behavioral aspects: Whether the agent infers the correct goals, makes progress towards them, and selects appropriate tools [03:04:00]. This relates to behavioral evaluation.
While ideally all these aspects should be evaluated, consistently evaluating even some of them can provide significant benefits [03:18:00].
The Model Context Protocol (MCP) Solution
The Model Context Protocol (MCP) offers a robust method for agent evaluation and stabilization [05:08:00].
The Stabilization Loop
MCP facilitates a stabilization loop where agents can dynamically self-improve and self-correct [13:35:00]. This process involves:
- Task Attempt: The agent attempts a specific task [04:35:00].
- Evaluation: The output of the task is evaluated by an evaluation engine [04:40:00].
- Feedback: The agent receives feedback in the form of a numeric score and an explanation of what went right or wrong [04:45:00].
- Improvement: The agent uses this feedback to improve its subsequent performance [04:57:00].
This loop relies on connecting agents to the evaluation engine via MCP [05:01:00].
Practical Applications of MCP
Manual Text Optimization Example
Using MCP via a UI (like Cursor), users can evaluate and improve text outputs from an agent or workflow [05:22:00].
- Listing Evaluators/Charges: MCP allows listing available evaluators (collections of evaluation criteria) [06:06:00].
- Evaluation and Improvement: By invoking a specific “charge” (e.g., “marketing message quality judge”), MCP can score the text, identify areas for improvement, and suggest optimized versions [06:41:00]. This process involves multiple evaluation passes, scoring the original and improved versions based on metrics like persuasiveness, writing quality, and engagingness [07:19:00].
Autonomous Hotel Reservation Agent Example
MCP can be integrated directly into an agent’s operation to enforce specific behaviors and policies [09:02:00].
- Problem Scenario: A hotel reservation agent for “Shire Hotel” is designed not to recommend a competitor (“Akma Hotel”) [09:47:00]. Without MCP, the agent might inadvertently mention the competitor [10:30:00].
- MCP Intervention: When MCP is enabled, the agent invokes evaluators like “hotel booking policy evaluator.” This prompts the agent to adhere to policies and avoid mentioning competitor hotels, even if not explicitly asked to do so [11:46:00]. The agent can often pick relevant evaluators from a list [11:53:00].
Strategic Approach to MCP Integration
To effectively leverage MCP for agent evaluation:
- Ensure a Robust Evaluation Platform: The chosen evaluation library or platform must be powerful enough to support a diversity of evaluators, their lifecycle maintenance, and optimization [12:34:00]. This includes optimizing evaluators themselves, as running many can incur costs [12:55:00].
- Start with Manual/Offline Use: Begin by running MCP manually (e.g., offline) to understand its mechanics and gain transparency into its operation [13:05:00].
- Attach Agents via MCP: Once familiar with the process, attach agents to the evaluation engine through MCP [13:24:00]. This integration leads to more controllable, transparent, and dynamically self-improving agents [13:30:00].
The Root Signals MCP server is available for free, and other implementations are expected to emerge, broadening the scope for integrating evaluation platforms into agent stacks [13:43:00].