One prompt, 100 agents: how graph engineering plus loops turns chatbots into systems
goyalshaliniuk · x · 2026-09-17
Using Google's Agent Graph concept as a springboard, the author lays out how to upgrade from single-prompt Q&A to a coordinated multi-agent system:
- Graph engineering: tasks are connected as nodes and edges; each node does one job (research, competitor analysis, fact-checking, writing, final review), and edges route outputs to the next stage.
- Loops: graphs decide "what happens next," loops decide "is the result good enough" — execute, check, find errors, retry — making workflows reliable.
- Parallel specialization: one prompt can fan out to dozens of agents, e.g. stages like product planning, UI design, frontend/backend, database, testing, bug fixing, deployment for building a SaaS app.
- Model swap: agents can run GPT, Claude, DeepSeek, or local open-source models; the graph owns the workflow while models are replaceable components.
Related event: Google's Agent Graphs: One Prompt, 100 Parallel Agents(4 posts)→
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