Agentic AI for the Enterprise

The Problem

Enterprises are rapidly moving beyond prompt-driven generative AI toward agentic AI systems that can plan, reason, use tools, and take actions on behalf of users or teams. These systems can chain multiple steps together without explicit instructions at each step. They can also invoke APIs, workflows, and enterprise tools to change system state, and even maintain context over long tasks and across interactions.

This shift creates new governance, accountability, and safety challenges. Traditional automation models (e.g., RPA, workflow tools) assume deterministic flows; predefined logic and branching; and limited (or no) autonomy to take actions without explicit human command.

Agentic systems break those assumptions. They behave less like “smart macros” and more like semi-autonomous digital workers in business processes. Existing risk frameworks, monitoring, and access controls were not designed for systems that can

• Decide which tools to call in what order

• Generate and execute their own plans

• Escalate (or fail to escalate) when uncertain

Without a clear operating model, agentic AI can quickly become ungovernable.

The Opportunity

Despite the risk, agentic AI represents a meaningful step-change in what enterprises can automate and augment:

• Throughput & Efficiency Multi-step tasks (e.g., onboarding, claims triage, procurement, support workflows) can be orchestrated end-to-end, with humans inserted only where judgment or approval is needed.

• Decision Quality & Consistency Agents can systematically retrieve relevant data, policies, and historical decisions, and enforce decision rules more consistently than fragmented, manual processes.

• Complex Workflow Automation Instead of manual handoffs between teams and systems, agents coordinate across tools, queue tasks, and track state, reducing coordination overhead and delays.

• Customer & Employee Experience Journeys that currently feel fragmented can be unified by agents that “remember” context across channels and episodes.

• Operational Resilience Well-governed agents can act as an additional layer of resilience – detecting anomalies, handling routine incidents, and escalating appropriately.

Importantly, agentic AI is practical today in constrained, low-to-moderate risk workflows. The largest business impact will likely arrive over the next 12–24 months as enterprises:

1. Learn where agents work well and where they fail

2. Mature governance and platform foundations

3. Gradually increase agent autonomy in carefully controlled domains

Early movers who start now will accumulate know-how, patterns, and guardrails which will pay dividends as complexity increases.

Together, we explore this topic more thoroughly in the full Substack article, including Enterprise Use Cases; Governance and Operating Models; Platform Foundations; and Risk, Safety, and Compliance. Read the full article here.