Agentic AI: Hype cooled. Now build what lasts.
2023 put AI on every board agenda thanks to ChatGPT. Then came the "year of agents" headlines. It didn't happen. The gap between marketing and working systems is still wide, and leaders feel it.
So what's truly new-and worth your attention? Simple: generative apps create content; agents take action. Actions move money, touch customers, and change systems. That's upside and risk in the same breath.
What is agentic AI?
Generative AI produces outputs. Agents operate with autonomy to do things. A content model lists restaurants and times. An agent books the table, pings your calendar, and emails the team.
When agents work, you get throughput without extra headcount. When they fail, the blast radius grows. A bad list is annoying. A bad action can expose data, distort operations, or trigger real costs-especially in health care, banking, or any system at scale where monitoring struggles to keep up.
Three stress-test questions for your agent strategy
1) What are the stakes?
Ask: What could go wrong if this action misfires? The stakes rise with complexity, regulation, and access to sensitive data. An internal agent that drafts notes and emails is low-stakes. Wrong notes waste time; wrong recipients create friction but are fixable.
Contrast that with a clinic agent sending post-visit summaries. One misaddressed email risks privacy, safety, and compliance. Do a full risk sweep on each proposed agent. Start with low-risk use cases and a tight task scope.
2) How easy is it to reverse an action?
Reversibility is your safety net. Deleting files, moving money, or sending sensitive info is hard-or impossible-to undo. That's where legal, financial, and reputational risk stack up fast.
We've already seen an agent delete a production database in the wild, prompting calls for a "plan/chat-only" mode before execution. Build for reversibility upfront: test in sandboxes, delay irreversible actions, and require human approval for anything you can't roll back.
3) How much autonomy can you afford?
Autonomy is a dial, not a switch. Predefined tools and workflows are more predictable than free-form reasoning with open endpoints. A tax agent spanning multiple tools, live regulations, and automatic submissions sounds efficient-until one wrong step creates an expensive mess.
Constrain early. Give the agent a narrow document set, a single filing tool, and clear acceptance criteria. Then expand access and freedom based on real performance data. Learn your safe autonomy level over time.
Build the guardrails: monitoring, frameworks, and people
Once you score each use case by stakes, reversibility, and autonomy, your monitoring requirements become obvious. High stakes and low reversibility demand real-time detection and fast shutdown paths. Borrow from industries that live this daily; automotive functional safety standards like ISO 26262 offer useful patterns for fail-safes and escalation.
Trust isn't a press release. It's architecture, evaluation, and policy that work together. Engage employees and frontline leaders early; they see both the best tasks to automate and the places where human control must stay. Join collective efforts like the Partnership on AI to align terms, tests, and safeguards across the industry.
Your rollout plan (that actually ships)
- Pick one low-stakes, high-volume workflow. Define the exact inputs, tools, and outputs. No edge cases in v1.
- Set permissions to least privilege. Blocklist risky actions. Require human sign-off for anything irreversible.
- Add a "plan/simulate" mode. Run dry-runs, canaries, and staged rollouts before full execution.
- Instrument everything: task success rate, intervention rate, error cost, and mean time to recovery.
- Stand up real-time failure detection with clear kill switches and rollback playbooks.
- Backups and time delays for destructive actions. Make reversal the default, not an afterthought.
- Governance: RACI for oversight, periodic risk reviews, red teaming, and documented incident response.
- Vendor due diligence: security posture, data handling, rate limits, auditability, and support SLAs.
- Upskill managers and ICs to supervise and improve agents-not just "use AI."
What great looks like in 12 months
- Three to five agent use cases in production, each with crisp KPIs and runbooks.
- Tiered autonomy policy by risk class, with gates for expansion.
- Automated monitoring that flags anomalies within minutes and routes to owners.
- Quarterly reviews that add scope where results prove out-and cut where they don't.
Next steps for executive teams
Get your first agent live where stakes are low, reversibility is high, and autonomy is tight. Use the data to earn broader privileges. Repeat.
For strategy frameworks and adoption playbooks built for leaders, see AI for Executives & Strategy. If you're standing up technical architecture and monitoring, the AI Learning Path for CTOs is a strong companion.
Let the hype cycle pass. Build the system. Make 2026 the year your agents deliver real outcomes-safely.
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