Loading...
Loading...
Loading...
Strategic Guide
The operational guide to persistent memory for long-lived AI agents.
Persistent memory systems, templates, and working-doc patterns for agents.
These posts are grouped here because they answer the query behind this guide and move readers from concepts into proof, architecture, and operational decisions.
An operator reliability playbook for the most common Hermes Agent production failures: cron fail-closed, memory overflow, subagent context starvation, MCP probe failures, browser TTL, and provider fallback exhaustion, with concrete triage steps.
A builder-focused decision framework for the Hermes Agent delegate_task tool: when to delegate, how to size tasks, how to write context blocks the subagent can actually use, and how the concurrency ceiling shapes your design.
Memory is where agent value compounds and where stale context, privacy, provenance, and hidden authority failures become dangerous.
When model, prompt, memory, tool, or policy context changes, the Agentic OS should decide whether old proof still applies.
Memory Mesh matters because agents appear collaborative in demos, but shared context silently degrades, conflicts, or becomes unverifiable under production pressure. This architecture is for system architects, staff engineers, and infrastructure teams deciding which components must exist and how ev…
Memory Mesh matters because agents appear collaborative in demos, but shared context silently degrades, conflicts, or becomes unverifiable under production pressure. This metrics and scorecards is for operators, executives, and trust-program owners deciding what to measure weekly and monthly so tru…
Memory Mesh matters because agents appear collaborative in demos, but shared context silently degrades, conflicts, or becomes unverifiable under production pressure. This operator playbook is for platform operators, deployment leads, and trust owners deciding how to roll this out in production with…
The templates and working-doc patterns teams need for is there a difference between rpa bots and ai agents in accounts payable so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for ai agent trust so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for ai agent reputation systems so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for roi of ai agents in accounts payable so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for fmea for ai systems so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for identity and reputation systems so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for failure mode and effects analysis for ai so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for reputation systems so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for persistent memory for ai so the category becomes operational, reviewable, and easier to scale responsibly.
The templates and working-doc patterns teams need for ai trust stack so the category becomes operational, reviewable, and easier to scale responsibly.
Persistent Memory is often confused with ephemeral context windows. This post explains where the boundary actually is and why that distinction matters in production.