pipeline
RAG Ingestion Pipeline
documents become chunks, embeddings, retrieval, and grounded answers
AniDiagram Layout Showcase
Live icon motion in visible cards. Open HTML for full-size details.
loop
Agent Reflection Loop
observe, plan, act, evaluate, and learn with a quiet feedback rhythm
hub-spoke
Agent Tool Hub
one agent coordinates search, browser, api, memory, and code tools
layered
LLM App Architecture Layers
interface, orchestration, tools, data, and observability stay separated
swimlane
Human-in-the-loop Approval Flow
user, agent, reviewer, and system each own a clear lane
compare
RAG vs Agentic RAG
compare retrieval-only flow with planning, tools, memory, and evaluation
matrix
AI Feature Priority Matrix
impact and effort separate quick wins from big bets
timeline
AI Product Launch Roadmap
alpha, beta, public launch, scale, and learning milestones
stack
AI Runtime Stack
ui, orchestration, model, tools, memory, and infrastructure layers
funnel
Lead-to-Agent Automation Funnel
capture, qualify, enrich, score, and hand off the right lead
sequence
API Tool Calling Sequence
user, agent, tool, api, memory, and output exchange ordered messages
er
Agent Memory Data Model
users, sessions, messages, memories, tool calls, and artifacts
network
Distributed Agent Runtime Mesh
workers, queues, caches, routers, and models coordinate across a mesh
agent-memory
Personalized Agent Memory Flow
the agent retrieves context, commits memory, and produces a grounded answer
agent-loop
Agent Loop Runtime Architecture
Trigger → think → guarded tool execution → observe → decide
layered-loop
Loop Engineering Operating Architecture
Show how automation, worktrees, skills, sub-agents, connectors, external state, and human judgement form a repeatable coding-agent loop.