The Agentic Stack Wars: Part Two - ARCHITECTURE

Key Takeaways

• All major AI agents share a five‑layer architecture.
• Vendors compete on owning specific layers, not new paradigms.
• Data and integration layers create deepest lock‑in and cost risk.
• Microsoft emphasizes governance; Google emphasizes full‑cloud execution.
• Perplexity provides a cloud‑agnostic, model‑routing middle ground.

Pulse Analysis

The rise of agentic AI has sparked a marketing frenzy, but beneath the glossy demos lies a familiar five‑layer stack that mirrors decades‑old enterprise software design. The data layer anchors the agent’s knowledge base, while the orchestrator translates intent into actionable plans. Connectivity bridges internal systems, the action layer executes tasks, and compute determines where processing occurs—on‑premise or in the vendor’s cloud. This architecture is the true battleground, not the hype around "revolutionary" models.

Each major player has chosen a different layer to dominate. Google’s Gemini Spark and Microsoft’s Copilot Studio embed the entire workflow in their massive cloud ecosystems, offering frictionless operation at the cost of deep lock‑in. Anthropic and OpenAI lean on desktop‑tethered runtimes, delivering high‑bandwidth assistance for active users but limiting background automation. Perplexity carves a niche by remaining cloud‑agnostic, routing tasks to the best‑fit model without demanding ecosystem submission. For enterprises, the choice hinges on how much control they need over data governance, integration complexity, and long‑term compute expenses.

The practical takeaway for decision‑makers is to map their own needs onto the five layers and ask where they are willing to outsource versus retain in‑house. Companies entrenched in Google Workspace or Microsoft 365 may accept full‑cloud stacks for speed, while consultancies and regulated firms should prioritize data and connectivity independence. As AI model pricing stabilizes, the real stickiness will come from the lower layers—data gravity, integration pipelines, and compute contracts. By treating the agentic stack as a modular map rather than a monolithic product, organizations can future‑proof their AI investments and avoid surprise cost spikes when vendors change terms.