What Is the Closed Learning Loop
Most AI coding agents are stateless — they forget everything between sessions. Hermes Agent breaks this pattern with a closed learning loop that continuously converts experience into reusable knowledge. The cycle works like this: the agent receives a task, executes it using multiple tools, evaluates the outcome, and if the task was complex enough (5 or more tool calls), auto-generates a structured skill document. That document is stored locally and retrieved automatically the next time the agent encounters a similar task.
The loop is “closed” because it feeds back into itself. Each execution refines the skill, each refined skill produces better execution, and each better execution generates even more accurate skill updates. Over time, the agent becomes measurably faster at recurring workflows. One Reddit user reported a 40% speed improvement on repetitive research tasks after just 2 hours of use — the agent had auto-generated 8 research-related skills that eliminated redundant tool calls and optimized source selection.
The conceptual flow is: Task → Execute → Evaluate → Generate Skill → Store → Retrieve on next similar task. This creates a flywheel effect where the agent gets better the more you use it, without any manual configuration required.
How Skills Are Auto-Generated
Skill auto-generation is triggered when a task meets two conditions: it required 5 or more tool calls to complete, and the agent evaluates the task pattern as likely to recur. The agent does not generate a skill for every complex task — one-off tasks like “fix this specific typo in line 47” are skipped because they have no reuse potential.
When generation triggers, Hermes captures several pieces of information from the completed task:
- The trigger phrase — what the user said or what condition initiated the task
- Context requirements — what files, data, or environment state the task needed
- The tool call sequence — every tool invocation in order, with parameters
- Decision points — where the agent chose between alternatives and why
- The output format — how results were structured and delivered
This information is compiled into a structured Markdown document and saved to ~/.hermes/skills/with a descriptive filename. The entire process is automatic — you do not need to tell the agent to learn or save anything.
Skill Document Anatomy
Every auto-generated skill follows the same Markdown structure. Here is an annotated breakdown of a real skill document for a research synthesis workflow:
# Research Synthesis
## Trigger
- User says: "research X and summarize"
- User says: "find information about X"
- Context contains: research request with topic
## Context Requirements
- Internet access (web search tool available)
- No special files or environment variables needed
## Steps
1. Perform web search for the topic (3-5 queries with varied phrasing)
2. Filter results — prefer primary sources, peer-reviewed content,
official documentation; discard SEO spam and thin content
3. Extract key points from top 5-8 sources
4. Cross-reference claims across sources for accuracy
5. Synthesize findings into a structured summary
6. Format with headers, bullet points, and source citations
## Expected Output
- Structured summary (300-500 words)
- Key findings as bullet points
- Source list with URLs
- Confidence assessment for each major claim
## Version History
- v1.0 (2026-03-15): Initial generation after 3 research tasks
- v1.1 (2026-03-18): Added cross-referencing step (step 4)
- v1.2 (2026-03-22): Improved source filtering criteriaEach section serves a specific purpose. The Trigger section tells the agent when to activate this skill. Context Requirements list prerequisites. Stepsprovide the exact workflow sequence. Expected Output defines what success looks like. And Version History tracks how the skill has evolved over time.
Self-Iteration: How Skills Evolve
Skills are not static. When Hermes executes a skill and discovers a better approach during the task — a more efficient tool call sequence, a better filtering criteria, or a more useful output format — it updates the skill document automatically. This is what makes the system a true learning loop rather than just a template library.
Self-iteration happens in three scenarios:
- Optimization — the agent finds a way to achieve the same result with fewer tool calls or faster execution
- Error correction — a step in the skill produces unexpected results, and the agent discovers a fix
- Expansion — the agent handles a variation of the task that the current skill does not cover and adds new branches
Every update is logged in the Version History section with a timestamp and description. If a self-iteration introduces a regression (the updated skill performs worse), you can manually revert to a previous version by editing the Markdown file. The conflict resolution strategy is last-write-wins, but the full change log is always preserved so nothing is permanently lost.