agentic AI

Overview

Agentic AI refers to AI systems designed to act with a degree of autonomy: interpreting goals, planning multi-step actions, using tools, interacting with software or the web, and completing tasks with limited human intervention. In practice, this often includes combinations of large language models, tool use, browser automation, workflow orchestration, memory, and guardrails. Rather than only generating content or answering questions, agentic systems are meant to do work across digital environments.

For AI Product Managers, agentic AI matters because it shifts product design from single-turn model outputs to end-to-end task execution. That creates new product questions around reliability, trust, observability, handoffs, human approval, and outcome measurement. The newsletter mentions suggest that by 2026 the conversation had moved beyond novelty: the focus was increasingly on whether deployed agents could be trusted in real workflows, from enterprise infrastructure to regulated domains like clinical operations.

Key Developments

• 2026-01-04: Pawel Huryn described a narrative shift in agentic AI: 2025 was centered on improving agent reliability, while 2026 was becoming about earning user and enterprise trust. He also noted that the agentic AI narrative was lagging behind actual deployments.
• 2026-03-17: NVIDIA expanded its partnership with Google Cloud to co-engineer infrastructure for the next generation of agentic AI, signaling that robust platform and compute foundations were becoming critical to production-grade agent systems.
• 2026-03-23: Teresa Torres highlighted Medable’s shift from e-consent and electronic assessments toward agentic AI, framing it as a way to reimagine clinical operations, reduce friction in drug development, and address patient access barriers.

Relevance to AI PMs

1. Design for trust, not just task completion PMs building agentic experiences need success metrics beyond "did the task finish?" Track intervention rate, approval rate, error recovery, escalation frequency, and user confidence. In many products, the differentiator is not autonomy alone but how safely and transparently the agent operates.

2. Plan around infrastructure and observability requirements
Agentic AI products depend on more than model quality. They require orchestration, tool permissions, browser or API execution layers, logging, evaluation harnesses, and failure monitoring. PMs should define what production readiness means for an agent and align early with platform, security, and infra teams.

3. Prioritize high-friction workflows where autonomy creates measurable value
The strongest use cases are often repetitive, multi-step, and operationally expensive tasks. PMs should look for workflows with clear goals, structured environments, and costly manual handoffs—then introduce human-in-the-loop checkpoints where risk is high, especially in regulated or customer-facing contexts.

Related

• ai-agents / agents: Closely related umbrella terms; agentic AI describes the broader approach and product pattern behind systems built as agents.
• browser-automation: A key enabling capability for agentic systems that operate directly in web interfaces and complete tasks across SaaS tools.
• openai-codex: Relevant as an example of tool-using AI for software tasks, illustrating how agent-like behavior can be embedded in developer workflows.
• vibe-coding: Connected through the broader shift toward AI-assisted execution, where systems move from ideation and generation into action.
• NVIDIA and Google Cloud: Important infrastructure players supporting the compute and platform layers needed for production agentic AI.
• Medable: A concrete enterprise example of agentic AI being applied in clinical operations and healthcare workflows.
• Teresa Torres and Pawel Huryn: Notable voices shaping how the concept is discussed, especially around product opportunity, deployment reality, and trust.