Agent Architecture
The system architecture for building AI agents, comprising multiple components such as models, storage, retrieval components, the memory layer and context engineering, with emphasis on customizable parts such as memory and tool use.
Core Facts
Timeline (last 90 days)
The Markdown injection attack is not a bug in some Markdown parser, but a missing trust boundary design at the agent architecture level.
Approaches to solving context loss typically include: retrieval-augmented generation (RAG) to dynamically pull relevant fragments from the codebase, building a code knowledge graph, or adopting an agent architecture capable of planning tasks autonomously.
Core rules need to be enforced through side channels (such as hard-coded validators, independent rule-checking layers, and deterministic verification) rather than relying on conversational context.
All Facts (5)
If tool granularity is too coarse, the AI agent will have difficulty precisely matching intent; if too fine, it increases the complexity of agent planning — balancing the two is the core challenge of tool design.
65%UnverifiedA typical agent architecture contains four layers: the perception layer, the planning layer, the execution layer, and the feedback layer.
60%UnverifiedThe Markdown injection attack is not a bug in some Markdown parser, but a missing trust boundary design at the agent architecture level.
50%UnverifiedApproaches to solving context loss typically include: retrieval-augmented generation (RAG) to dynamically pull relevant fragments from the codebase, building a code knowledge graph, or adopting an agent architecture capable of planning tasks autonomously.
50%UnverifiedCore rules need to be enforced through side channels (such as hard-coded validators, independent rule-checking layers, and deterministic verification) rather than relying on conversational context.
50%