Multi-agent systems improve reasoning through collaborative debate. While costly and slow, they are vital for high-stakes tasks requiring extreme accuracy.
Reflexion architecture uses self-correction and memory to fix errors, but high costs and latency make it best for deterministic tasks.
Plan-and-Execute architectures prioritise structured, hierarchical workflows over reactive loops, delivering superior predictability and parallel execution for complex, multi-step business tasks.
ReAct architecture boosts performance via iterative reasoning, yet faces high costs and latency. It’s best for narrow, specialised research tasks.
BDI architecture offers a transparent, resource-efficient framework for goal-directed RAG, though it requires stable environments and rigorous upfront plan engineering.
Microsoft 365 E7 offsets AI infrastructure costs by bundling Copilot and governance, though consumption-based pricing creates significant fiscal unpredictability.
Ignore ‘SaaSpocalypse’ hype. SaaS remains your stable system of record; use AI ‘vibe-coding’ only for niche, high-value edge innovations.
Effective AI deployment requires matching specific request types to varied agentic architectures, balancing performance, cost, and latency for optimal outcomes.
Microsoft’s complex five-layer licensing hierarchy causes 15-40 per cent budget waste. Mastering these layers ensures commercial competence and prevents costly capability duplication.