Agentic AI / autonomous workflows
Multi-step tool-using agents driving new demand curve
The 2025-2026 shift from single-turn chat to multi-step autonomous agents (using tools, memory, and MCP) multiplies token consumption per task and is the demand engine behind inference growth. Agentic workloads underpin the revenue theses of Salesforce, ServiceNow, Sierra, and coding tools. Reliability, evaluation, and liability for agent actions are the open governance questions.
How it fits the stack
Agentic AI / autonomous workflows with what it depends on (above) and what it feeds (below). The figure renders as a crawlable diagram and upgrades to an interactive 3D graph as it scrolls into view.
Agentic AI / autonomous workflows in the AI stack. Agentic AI / autonomous workflows with its immediate upstream dependencies (top) and downstream dependents (bottom) in the AI value chain. Hover a node in 3D, or read the full relationships below.
Graph data (text) — 8 entities, 7 relationships
- Agentic AI / autonomous workflows —depends on→ Inference economics / token deflation
- Agentic AI / autonomous workflows —depends on→ Model Context Protocol (MCP)
- Agentic AI / autonomous workflows —used by→ Vector databases
- Abridge / ambient clinical AI —uses→ Agentic AI / autonomous workflows
- Salesforce Agentforce —uses→ Agentic AI / autonomous workflows
- ServiceNow —uses→ Agentic AI / autonomous workflows
- Sierra (Bret Taylor) —uses→ Agentic AI / autonomous workflows
Depends on ↑ · 3
Context — capital, rivals, policy · · 1