Gartner Top Strategic Technology Trends for 2025: Agentic AI

Agentic AI refers to goal-driven software entities—“digital coworkers”—that can plan, decide, and act on an organization’s behalf with minimal supervision. Unlike classic chatbots or coding assistants that respond only to prompts, agentic systems combine models (e.g., LLMs) with memory, planning, tools/APIs, sensing, and guardrails so they can pursue outcomes, not just generate content.

Why now

Vendors are equipping assistants with planning and tool-use. Startups offer agent-building platforms; hyperscalers are weaving agentic capabilities into their stacks. As this matures, AI shifts from advisory to operational—able to analyze data across systems overnight, execute workflows, and report what it finished versus what still needs a human decision.

Opportunity landscape

1. Performance gains that compound. Agents learn from feedback and environment, so quality and speed improve over time.

2. Decision acceleration. They scan complex datasets, identify patterns, and take next actions, reducing modeling overhead and time-to-impact.

3. Workforce augmentation. Natural-language orchestration lets teams manage intricate projects and micro-automations without deep tooling expertise.

4. Scale and coverage. Multiagent systems coordinate many specialized agents—each perceiving and acting—to tackle goals no single agent could handle.

5. Experience automation. From purchase to follow-up, agents can personalize outreach, time communications, and launch cross-sell offers, closing the loop without human intermediaries.

Strategic planning assumptions (2028 horizon)

• One-third of enterprise apps will embed agentic AI (up from <1% in 2024).

• Machine customers (agentic buyers) will handle about one-fifth of storefront interactions.

• At least 15% of day-to-day work decisions will be made autonomously.

What changes in practice

Workflows will be designed for agents first, with humans inserted at high-value control points. Collaboration becomes tri-directional: humans→agents, agents→agents, and agents→humans. Software developers feel early impact as coding assistants evolve into agents that open tickets, refactor code, run tests, and submit merge requests. In operations, agents reconcile data, tune campaigns, or remediate incidents while you sleep—escalating only what truly needs judgment.

Risks and pitfalls

• Governance drift. Without a registry, ownership model, and lifecycle controls, organizations can repeat the RPA “bot sprawl” problem.

• Data quality & security. Agents act from enterprise data and tool access; poor data or weak identity controls can cause harmful actions.

• Safety threats. Prompt injection, jailbreaks, data exfiltration, and agent-to-agent adversarial behavior demand new defenses.

• Customer experience missteps. Autonomy can alienate customers if journeys aren’t intentionally designed.

• Change management. Employees may resist perceived loss of control; roles must be clarified and upskilling funded.

Design principles

• Agency is a spectrum. Decide, per workflow, what agents can observe, propose, approve, and execute.

• User-in-the-loop by default. Start with propose/preview modes; graduate to execute-with-revert once reliability metrics pass thresholds.

• Guardrails first. Enforce scoped permissions, environment sandboxes, rate limits, and bounded tool catalogs. Require provenance logging for every agent action.

• Explainability & monitoring. Track goals, plans, tool calls, outcomes, and self-critique notes; alert on drift and unusual chains of actions.

• Composable architecture. Use an orchestration layer that connects apps, data, identity, EPM/ITSM, and observability—so agents act through governed interfaces.

Near-term actions (next 6–12 months)

1. Map candidate workflows where scale/latency matter and high-quality data already exists (support ops, marketing ops, finance close, IT service, supply planning).

2. Define levels of agency for each: observe → recommend → execute with human approval → execute with rollback.

3. Stand up an “AgentOps” discipline: registry, versioning, policy as code, red-teaming, simulation testing, and automated kill-switches.

4. Harden identity and access. Give every agent a first-class identity, least-privilege roles, secrets management, and audit trails.

5. Measure value. Instrument outcomes (cycle time, error rate, SLA adherence, revenue lift) and require business owners for every agent.

6. Pilot multiagent patterns. Try specialist swarms (planner, tool-user, reviewer) with explicit protocols for delegation and critique.

Bottom line

Agentic AI moves enterprises from “generating insights” to taking action. The advantage goes to leaders who embed agency into architecture and governance—treating agents as Tier-1 digital coworkers with clear scopes, telemetry, and accountability—so performance scales without sacrificing safety, trust, or customer experience.

Access the Gartner whitepaper here.

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