If “cloud” was the defining infrastructure bet of the 2010s, “agentic AI” is becoming the defining product bet of the mid-2020s. Microsoft, Google, OpenAI, Anthropic, Salesforce, ServiceNow, and a long list of vertically-focused companies have each made agentic systems central to their product roadmaps. The coverage has been breathless; the actual capability has been mixed.
This piece tries to do something useful: explain what “agentic” actually means in practice, why the platform incentives are so strong, what’s working today versus what’s still aspirational, and how to read the marketing without being misled.
What “agentic AI” means when you strip the marketing
The word “agent” gets used across a very wide capability spectrum. In the most minimal sense, an “AI agent” might just mean an LLM with a tool call (web search, code execution). In the most expansive sense, it means an autonomous system that plans multi-step workflows, delegates subtasks to specialized sub-agents, monitors its own outputs, and corrects course when things go wrong.
The meaningful practical definition sits in the middle: an agentic system can complete a goal-directed, multi-step task with limited human intervention, using a combination of reasoning, tool use, and memory.
What this enables that chat interfaces don’t
- Multi-step workflows: researching a topic, synthesising it, formatting output, and sending it — without the user touching each step
- Tool orchestration: calling APIs, running code, querying databases, reading files as part of a coherent workflow
- Parallelisation: breaking a task into independent subtasks and running them simultaneously
- Loop closing: detecting when an intermediate step failed and retrying with a different approach
Why the platform incentive is so strong
Every major software platform company has the same strategic problem: AI features at the assistant layer (autocomplete, summarisation, Q&A) are rapidly becoming commoditised. The moat shifts to whoever controls the workflows.
An agentic layer that is embedded in your CRM, project management tool, communication platform, or development environment becomes a switching cost and a data flywheel simultaneously. When an agent learns the patterns of your sales process, your sprint cadence, your document templates — that context is hard to replicate elsewhere.
This is why “agents” are simultaneously a product strategy and a retention strategy for platform companies.
The capability gap: what’s real in 2026
Works well today
- Single-domain task completion with clear success criteria (draft this email, research this company, run this query)
- Developer tooling (code generation, test writing, documentation) with human review loops
- Structured data workflows where inputs and outputs are well-defined
- Customer service escalation with defined playbooks
Works with supervision
- Multi-step research and synthesis tasks across diverse sources
- Cross-tool workflows where each integration is tested but failures need recovery
- Personalisation workflows with contextual history
Still mostly aspirational
- Fully autonomous long-horizon planning without human checkpoints
- Reliable handling of novel situations outside training distribution
- Consistent performance on high-stakes tasks without hallucination risk
- Multi-agent coordination at scale without brittleness
What the investment data actually shows
The 10 most funded AI startups of 2026 are disproportionately agentic AI infrastructure and vertical applications — not general-purpose chat products. The infrastructure layer (orchestration, memory, tool integration, observability) and vertical applications (legal, finance, healthcare, software engineering) are attracting the most capital, which signals where investors see durable value versus commoditised features.
FAQ
For well-defined, low-stakes workflows with human review: yes, with appropriate guardrails. For high-stakes or fully autonomous deployment: requires careful scoping and monitoring. The risk profile varies significantly by domain.
Anthropic and OpenAI lead on underlying model capability for agentic tasks. Microsoft (Copilot ecosystem), Google (Gemini Workspace agents), and Salesforce (Agentforce) lead on enterprise distribution. The advantage in any specific vertical depends on the integration depth.
The more likely near-term outcome is that agents become the interface layer within SaaS tools, rather than replacing them. The underlying data, workflow state, and permissions still live in the existing tools.