I Ran Hermes Agent on the Same Task for 7 Days. The Skill File on Day 7 Looked Nothing Like Day 1.

2026-05-18 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

Hermes Agent, an open-source AI agent developed by Nous Research, demonstrates a unique ability to autonomously improve its performance on recurring tasks over time, unlike other frameworks such as LangChain, AutoGen, or CrewAI. An experiment involved running Hermes Agent on a daily news aggregation task for seven days, observing its "skill file" evolve from a 12-line basic procedure to a 62-line intelligent process. Key improvements included self-implementing source filtering, creating a formal scoring rubric for relevance, adding negative query filters (e.g., "-ChatGPT -Gemini"), and incorporating a 7-day deduplication window. This learning is facilitated by a closed learning loop architecture comprising evolvable skills, persistent memory, dialectic user modeling via Honcho, and autonomous nudges, all stored locally and MIT licensed.

Key takeaway

For AI Engineers and MLOps professionals evaluating agent frameworks for recurring, complex tasks, Hermes Agent offers a distinct advantage by autonomously improving its operational skills over time. Unlike stateless alternatives, Hermes Agent's persistent learning loop means your agent becomes more efficient and tailored to your specific needs without manual intervention. Consider deploying Hermes Agent for workflows where compounding value from session-to-session improvement is critical, especially if you prioritize owning the accumulated intelligence locally.

Key insights

Hermes Agent autonomously refines its operational skills and preferences across sessions, unlike stateless AI agent frameworks.

Principles

• AI agents can achieve compounding value through persistent learning.
• User preferences can be inferred and encoded into agent behavior.
• Local ownership of agent intelligence enhances portability and control.

Method

Hermes Agent employs a closed learning loop: it executes a task, records its trajectory, extracts reusable skills, and updates these skills based on observed outcomes, leveraging persistent memory and user modeling.

In practice

• Automate recurring tasks that benefit from continuous refinement.
• Use Hermes Agent for tasks requiring evolving search strategies.
• Review generated skill files to understand agent's learned logic.

Topics

• Hermes Agent
• AI Agent Frameworks
• Skill File Evolution
• Closed Learning Loop
• Autonomous Learning

Code references

• plastic-labs/honcho
• NousResearch/hermes-agent

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.