Multi-Agent AI Systems Explained: Context Engineering and Cost Control

Autonomous multi-agent AI workflows require precise context engineering to balance accuracy, latency, and API cost control. This analysis explains passive context inheritance, token accumulation across agent chains, and how unmanaged memory sharing leads to exponential cost growth and context poisoning. Learn how frameworks like Anthropic Agent SDK, LangGraph, OpenAI Agents SDK, and Pydantic AI handle context boundaries, dependency injection, and structured state transfer. The video also covers confirmation bias in validation agents, structural amnesia, and the verification probe pattern for zero-trust evaluation. These patterns define scalable, production-grade AI systems built on controlled context propagation and deterministic state management. 0:00 Shift to autonomous multi-agent workflows 0:08 Context engineering as the core bottleneck 0:24 Accuracy, latency, and cost trade-offs 0:41 Passive context inheritance risks 0:58 Exponential token accumulation across agents 1:28 Context poisoning and reasoning degradation 1:48 Context control paradigms and architectures 2:48 Framework defaults and cost overruns 3:18 Hallucination cascades and logic drift 5:42 Verification probe and structural amnesia 🤖 Multi-agent workflows 🧠 Context engineering 📉 Token cost scaling ⚠️ Context poisoning risks 🔐 Zero-trust verification Controlling multi-agent AI systems requires disciplined context isolation, deterministic routing, and structured data mapping. Teams that implement verification probes, dependency injection, and scoped memory flows reduce hallucination risk and token waste. Scalable AI infrastructure depends on enforcing context boundaries before exponential cost and logic drift undermine long-term system performance. #AIAgents #AIArchitecture #MachineLearning