"Bannon argued that much of the current confusion stems from collapsing very different behaviors and risk profiles under the same labels. Bots were described as scripted responders that react to predefined triggers, while assistants collaborate with humans and remain largely under human control. Agents, by contrast, are goal-driven actors capable of making decisions and taking actions across systems. Everyone is talking about AI 'productivity.' Very few are talking about the architectural amnesia that comes with it. - Tracy Bannon"
"To reify, Bannon outlined a set of autonomy patterns that commonly appear across the software development lifecycle. These ranged from AI-assisted tools embedded in existing workflows, through task-level agents that operate within bounded scopes, to multi-agent orchestration that coordinates end-to-end flows, and finally to mission-level autonomy where systems plan, optimize, and adapt toward higher-level goals. A central theme of the talk was that autonomy does not fail on its own; failures occur when autonomy grows faster than architectural discipline."
"Bannon described this gap as producing what she called "agentic debt". She connected agentic debt to familiar problem areas such as identity and permissions sprawl, insufficient segmentation and containment, missing lineage and observability, and weak validation and safety checks. Bannon tied this risk to broader industry trends, noting research indicating that a large majority of technology decision-makers expect technical debt severity to rise in the near term due to AI-driven complexity. She argued AI does not introduce fundamentally new failure modes, but it magnifies existing"
Rapid adoption of AI agents is reshaping software systems and risks repeating familiar architectural failures if different AI behaviors are treated as interchangeable. Bots are scripted responders triggered by predefined events. Assistants collaborate with humans and remain under human control. Agents are goal-driven actors that make decisions and take actions across systems. Autonomy patterns span AI-assisted embedded tools, task-level agents within bounded scopes, multi-agent orchestration, and mission-level autonomy that plans, optimizes, and adapts toward higher-level goals. Failures arise when autonomy outpaces architectural discipline, producing "agentic debt" that magnifies identity, permissions, segmentation, observability, and validation issues.
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