Agentic AI

Overview

Agentic AI refers to AI systems designed to act with a degree of autonomy to complete multi-step tasks, often by combining reasoning, memory, tool use, API calls, and browser interaction. Rather than only generating text or recommendations, agentic systems can take actions such as navigating interfaces, retrieving information, executing workflows, and coordinating across software environments to achieve an outcome.

For AI Product Managers, agentic AI matters because it shifts product design from single-turn model interactions to end-to-end task completion. This raises the bar on reliability, trust, observability, safety, and human oversight. In the newsletter coverage, the framing evolves from technical capability and infrastructure readiness toward a more mature question: not whether agents can be deployed, but whether users and organizations can trust them in production for consequential workflows.

Key Developments

• 2026-01-04: Pawel Huryn described a narrative shift in agentic AI: 2025 was centered on agent reliability, while 2026 would be about earning trust. The point was that the market narrative lagged behind real deployments, suggesting that operational confidence had become the next strategic challenge.
• 2026-03-17: NVIDIA expanded its partnership with Google Cloud to co-engineer infrastructure for the next generation of agentic AI, signaling that large-scale agent deployment depends not just on models, but on cloud, compute, orchestration, and systems engineering.
• 2026-03-23: Teresa Torres highlighted Medable’s move from e-consent and electronic assessments toward agentic AI in clinical operations, showing how agents are being positioned to reduce friction in complex, regulated workflows such as drug development and patient access.

Relevance to AI PMs

1. Design for trust, not just task success AI PMs need to define product requirements beyond raw completion rate: approval checkpoints, fallback behavior, action logs, explainability, user controls, and escalation paths all become core parts of the experience when agents act autonomously.

2. Evaluate the full execution stack
Shipping agentic AI requires more than model selection. PMs must assess browser automation, tool permissions, workflow orchestration, monitoring, latency, error recovery, and infrastructure dependencies to ensure the system performs reliably in real environments.

3. Target high-friction workflows with measurable ROI
The best early opportunities are repetitive, multi-step tasks with clear success criteria, such as customer operations, internal tooling, or regulated process support. PMs should prioritize use cases where autonomy reduces manual effort while still allowing structured oversight.

Related

• Medable: An example of a company applying agentic AI to clinical operations, illustrating its use in regulated, high-stakes domains.
• Teresa Torres: Highlighted Medable’s strategic shift, connecting agentic AI to product discovery and workflow redesign.
• NVIDIA: Represents the infrastructure and compute layer required to power production-grade agentic systems.
• Google Cloud: Connected through infrastructure partnership work that supports large-scale agent deployment.
• AI Agents / Agents: Closely related umbrella terms; agentic AI describes the broader approach and system design philosophy behind autonomous agents.
• Browser Automation: A key capability in many agentic systems, enabling agents to interact with web interfaces and complete real tasks.
• OpenAI Codex: Relevant as a tool-oriented AI system associated with autonomous software and workflow execution patterns.
• Vibe-coding: Related through the broader shift toward AI-assisted creation and execution, though vibe-coding is more about generation flow than autonomous action.
• Paweł Huryn: Framed the market transition from focusing on reliability to focusing on trust in deployed agentic systems.