GSDC Mentor Connect session: Agentic AI use cases, trends, concepts

Agentic AI describes AI systems that can pursue goals autonomously with limited human supervision coordinating steps, making decisions, and acting across tools and data sources to complete multi-step tasks. 

Think of these systems as goal-driven “digital teammates” that can plan, monitor, and execute on objectives rather than simply replying to prompts. IBM

• Agency & Goals: Agents are given objectives (explicit or implicit) and a success criterion; they break goals into subtasks and iterate.
   
• Planning & Orchestration: Agents autonomously plan sequences of actions, call APIs, and orchestrate other services.
   
• Multi-agent coordination: Multiple agents can work in concert, each handling a specialized subtask.
   
• Closed-loop feedback: Agents monitor outcomes and adjust behavior without constant human steering.
   
• Safety & governance primitives: Constraints, human-in-the-loop checkpoints, and guardrails limit undesired actions.

These are the agentic AI key concepts that appeared repeatedly in the session slides.

Below are concise agentic AI use cases we discussed, presented as short descriptions, so you can map them to your organization.

• IT & DevOps automation
  Agents monitor CI/CD pipelines, detect failed deployments, triage logs, open remediation tickets, and even run rollback scripts when thresholds are breached. (One of the earliest widespread enterprise uses is DevOps automation.) Default
• Autonomous service-desk agents
  Beyond scripted chatbots, agentic IT help desks can diagnose multi-step problems (environment checks, permission resets, configuration changes) and coordinate with systems to resolve incidents. budibase.com
• Supply-chain orchestration
  Agents re-route shipments, negotiate with vendors (via API), and rebalance inventory when disruptions occur, acting faster than humans across global time zones. lasso.security
• Customer resolution & commerce agents
  Shopping or booking agents can search, compare, and complete purchases on behalf of users, handling multi-step transactions end-to-end. This use case is already reshaping e-commerce flows. Financial Times
• Finance & analytics automation
  Agents prepare regulatory reports, reconcile ledgers, and flag exceptions, orchestrating data pulls, calculations, and human approvals in sequence. akka.io
• Personal productivity copilots (agentic assistants)
  Agents proactively schedule, summarize, and follow up on actions, not simply respond when asked.

These are practical examples of use cases for agentic AI that organizations in 2025 are piloting and scaling.

Agentic AI is becoming a boardroom topic. Key market signals from recent industry reporting:

• Analysts and advisory firms show sharp growth in agentic deployments across IT, finance, retail, and manufacturing; many early POCs concentrate on DevOps and IT service management. Default
   
• Surveys and enterprise reports indicate adoption inflection points. A noticeable share of organizations reported deploying agents in 2025, with forecasts suggesting rapid expansion as maturity and governance improve. KPMG
   
• Vendor and tooling activity is intense: specialized agent frameworks, off-the-shelf agent platforms, and integrations across observability, ERPs, and collaboration tools are proliferating. Recent market write-ups list dozens of agentic tools and vertical solutions. lasso.securityMarket.us

• From assistant → autonomous collaborator: Copilots will move from “assist” to “act” in clearly defined scopes (e.g., automated renewals, procurement automation). McKinsey & Company
   
• Industry-specific agents: Banking, retail, and manufacturing will see specialized agents that embed domain logic and compliance rules. Early ROI is strongest where rules and data are mature. Default
   
• Agent ecosystems: Expect marketplaces of modular agents and agent orchestration layers that let companies compose agents for complex workflows. Everest Group Reports
   

Agentic systems open new capabilities and new risks. Key guardrails to adopt before scaling:

• Define allowed action scopes (what agents may and may not do).
   
• Log intent and actions (auditability) for every agent decision.
   
• Implement human-review gates for high-risk flows (payments, legal actions).
   
• Monitor drift and implement automated rollback on anomalous behavior.
   
• Build a data-privacy and compliance map (agents will touch many systems).

This implementation checklist echoes the governance themes from our Mentor Connect discussion.

To design, operate, and govern agents, you’ll need hybrid skills: systems engineering, prompt & chain-of-thought design, orchestration, API integration, and AI safety. 

We also expect the emergence of role-based credentials and agentic AI certification tracks that combine engineering with governance and ethics. 

For L&D teams, relevant learning pathways include hands-on labs, scenario-driven playbooks, and cross-functional simulation exercises.

• Pilot where value and data quality are high (IT, finance, supply chain).
   
• Pair agent pilots with clear KPIs (time-to-resolution, cost per transaction, error rate).
   
• Build a governance sprint (30–90 days) to establish policy, logging, and human-in-loop controls.
   
• Invest in internal skills: orchestration engineers, agent testers, and audit analysts.

Agentic AI is not merely a new label for existing tools: it is a capability shift toward autonomous, goal-driven systems that can materially change workflows. 

For practitioners and leaders, focus on targeted pilots, governance-first deployments, and developing hybrid talent that combines engineering know-how with ethical oversight. 

If you attended the GSDC Mentor Connect session, use the slides and labs as a starting blueprint and consider joining our next deep-dive to translate a pilot into production safely.