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How to Choose Between a Deterministic Workflow, One Agent, and Multiple Agents

TL;DR Choose a deterministic workflow when the path is known, repeatability matters, latency must stay low, and every transition needs to be explainable. Choose one agent when the task is variable or open-ended, but the work still fits within one domain, one security boundary, and one accountable product team. Choose multiple agents only when specialization,

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AgentOps Is the New Day-2 Operations: A Control Model for AI Agents

AI teams are starting to ship agents like application features. That sounds normal until the agent has tools, memory, workflow access, delegated permissions, and the ability to take action across business systems. At that point, the agent is no longer just a prompt, a chatbot, or a model endpoint. It is an operational component. A

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Graph Engineering for AI Agents: Beyond the Single-Agent Loop

AI-agent development has progressed through overlapping phases: prompt engineering, context engineering, tool use, autonomous loops, memory systems, and multi-agent coordination. A newer focus is graph engineering, which treats AI applications as explicitly designed workflows rather than a single autonomous agent. Graph engineering defines how agents, tools, deterministic functions, validators, data sources, and humans coordinate to

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