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Blog Topic
Memory systems, templates, and operating patterns for long-lived agents.
24 metadata-ranked posts in this topic
Ranked for relevance, freshness, and usefulness so readers can find the strongest Armalo posts inside this topic quickly.
When model, prompt, memory, tool, or policy context changes, the Agentic OS should decide whether old proof still applies.
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.
When an agent's decision is contested, the only defensible answer is a signed manifest of exactly what was in its context at the moment it acted.
Memory is where agent value compounds and where stale context, privacy, provenance, and hidden authority failures become dangerous.
Swapping one agent for a successor should not start the new agent blind. The cold-boot pattern transfers capability-scoped memory, attestation, and context gradually.
When agent A asks agent B for context, the wrong primitive is transfer. The right primitive is a scoped, time-bound, revocable loan with full audit trail.
Re-embedding a corpus changes vector positions. Old memory pointers stop resolving. The dual-index migration pattern handles cutover without losing accumulated context.
How persistent memory AI and portable reputation reinforce each other when agents need trust that survives across workflows and platforms.
Individual agent memory resets at context boundaries. Memory Mesh doesn't. Armalo's shared memory substrate gives multi-agent systems persistent, conflict-resolved, cryptographically verifiable knowledge that compounds with every operation — producing collective intelligence that no collection of amnesiac solo agents can match.
How to use persistent memory AI in multi-agent systems without creating a shared hallucination layer.
Persistent Memory is often confused with ephemeral context windows. This post explains where the boundary actually is and why that distinction matters in production.
The templates and working-doc patterns teams need for persistent memory for agents 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.
AI agent supply chains extend far beyond code packages — skills, tool wrappers, memory artifacts, and prompt context are all attack surfaces. This guide covers 8 attack vectors with real CVEs, NIST/CISA framework application, a step-by-step kill chain, and 10 defense-in-depth controls for teams operating autonomous agents at scale.
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 Governance for AI Agents through a security and governance lens: who should be allowed to write, read, approve, expire, and revoke durable agent memory.
Context Provenance and Expiry for AI Agents through a code and integration examples lens: how to know where a critical fact came from and when it should stop being trusted.
Memory Governance for AI Agents through a full deep dive lens: who should be allowed to write, read, approve, expire, and revoke durable agent memory.
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…
Context Provenance and Expiry for AI Agents through a comprehensive case study lens: how to know where a critical fact came from and when it should stop being trusted.
Memory Governance for AI Agents through a architecture and control model lens: who should be allowed to write, read, approve, expire, and revoke durable agent memory.
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…
Memory Mesh matters because agents appear collaborative in demos, but shared context silently degrades, conflicts, or becomes unverifiable under production pressure. This security and governance is for security leaders, governance owners, and regulated buyers deciding what must be enforced in polic…
Trust Algorithms
A scoring frame for the difference between model capability and the trust infrastructure required to authorize consequential agent work.