2 min read
Building an execution-memory loop: how outcomes feed back into recall
A memory layer that only stores plans, not outcomes, is missing the half of the record that actually teaches an agent anything.
By Agent Software
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2 min read
Why local-only memory beats cloud context windows
A bigger context window still forgets everything the moment your session ends — persistent memory is a different problem entirely.
By Agent Software
2 min read
The future of AI coding infrastructure: memory, evals, terminals, and orchestration
The future of AI coding looks less like one perfect agent and more like a stack of infrastructure layers that make agents usable.
By Agent Software
2 min read
Agent Wispr and Agent Brain together: faster capture, better recall
Fast input becomes more valuable when it does not disappear. That is the practical link between Wispr and Brain.
By Agent Software
2 min read
Building around MCP without pretending MCP solves everything
MCP can improve interoperability, yet teams still need clear thinking about trust, workflow boundaries, and system behavior.
By Agent Software
2 min read
Agent Brain semantic caching: what it is, when it helps, and when it does not
Semantic caching is valuable when similar work repeats, but it becomes dangerous when superficial similarity hides changed requirements.
By Agent Software
2 min read
Open problems in agent memory: stale context, conflict resolution, and trust
Agent memory remains unsolved around freshness, contradiction, and trust boundaries even when retrieval quality improves.
By Agent Software
2 min read
Designing a shared memory layer for Claude, Codex, Gemini, and Cursor
Cross-tool memory gets useful only when it distinguishes durable context from stale noise and keeps retrieval tied to real workflow needs.
By Agent Software
2 min read
What Agent Brain solves today, and what it still does not solve
Agent Brain helps with shared memory and semantic recall, but it does not erase stale context, ambiguity, or the need for human judgment.
By Agent Software
3 min read
The coordination problem in AI coding: why separate tools fail when context is fragmented
The hard problem in AI coding is not getting one model answer. It is coordinating state across tools, time, and human decisions.
By Agent Software