I recently completed a consulting engagement with an organization that wanted their people spending time on value-added tasks instead of administrative work. The problem was that approvals were being bounced around. If they had someone at the front, they took care of it; if not, the task stalled.
The workflow was simple on paper but painful in practice: Customer wants a refund > emails the company > team evaluates > if approved, gathers purchase details (date, payment method, original PO) > compiles documentation > sends to accounting for another review > accounting processes refund.
Larger refunds from contractors were handled differently and weren’t on the table for automation. But these small refunds (typically under $50) were eating up hours of rework as multiple people validated the same information.
The senior managers had heard about AI agents and wanted to automate this. After spending a few days understanding their process and goals, I presented a roadmap for implementing agentic loops to handle these refunds, freeing their team to focus on higher-value work.
If you’re an operations leader, you’ve likely faced a similar disconnect between the hype around “Agentic AI” and the operational reality of getting it to work reliably. Here’s the playbook I used.
1. The Ops-Friendly Definition: From Linear Scripts to Agentic Loops
Most automation you’ve used in the past was linear: If X happens, do Y. If Y fails, the whole process crashes. Agentic AI is different because it operates in loops.
Think of an agentic loop as a four-step cycle that mirrors how your best senior agents think:
- Observe: The agent looks at the current state (e.g., “The customer wants a refund, but the order status is ‘Shipped'”).
- Reason: It uses its logic to decide the next best step based on your policies.
- Act: It performs an action (e.g., checks the tracking API or drafts a denial email).
- Reflect: This is the game-changer. The agent looks at the result of its action. Did it work? If not, why? It then loops back to “Reason” to try a different approach or escalate.
Why this matters for Ops: A linear bot breaks when it hits a snag. An agent in a loop adapts. In my client’s case, if the original PO wasn’t attached to the email, the agent didn’t fail, it looped back to search the CRM by invoice number or customer name before escalating. That’s the difference between a script that says “Error 404” and a junior agent who finds another way.
2. Busting the Myths
- Myth: “It’s just RPA with LLMs.”
- Reality: Robotic Process Automation (RPA) is brittle, if a button moves on a screen, the bot breaks. Agentic AI understands intent. Because it operates in a loop, it can “notice” a change in the UI or data structure and adjust its path rather than crashing.
- Myth: “It will replace all my agents.”
- Reality: Agentic AI replaces tasks, not judgment. It handles the 80% of routine, structured work, freeing your human team to focus on the 20% that requires empathy, complex negotiation, and high-stakes decision-making. For my client, the accounting team still reviewed every refund over $50, the agent simply eliminated the rework for everything below that threshold.
3. Three Concrete Pilot Opportunities
Don’t try to boil the ocean. Start with high-volume, low-risk areas where success is easy to measure.
- Automated Tier-1 Resolution: Think password resets, order status checks, or basic billing inquiries. These are binary tasks with clear “happy paths.” An agent can resolve these instantly through its observe-act-reflect loop, reducing wait times.
- Post-Call Work Summarization: Instead of agents spending 5–10 minutes after every call typing notes, an agentic loop can listen to the conversation, extract key data points, update the CRM fields, and log the disposition automatically. The “reflection” step ensures the summary actually matches what was discussed.
- Proactive Account Outreach: Use behavioral triggers to identify at-risk clients. If a high-value client’s usage drops, the agent can draft a personalized check-in email. A human reviews and hits send, turning a reactive support model into a proactive success model.
4. The Decision Matrix: When to Use What
|
Feature
|
Agentic AI
|
Human-Assisted Prompts
|
|---|---|---|
|
Complexity
|
Low to Medium (Structured)
|
High (Ambiguous)
|
|
Risk Level
|
Low (Reversible actions)
|
High (Regulatory/Financial)
|
|
Emotional Stake
|
Low (Transactional)
|
High (Empathy required)
|
|
Goal
|
Efficiency & Scale
|
Judgment & Relationship Building
|
5. The Phased Rollout: Building Trust Before Autonomy
The biggest risk in agentic AI isn’t the technology, it’s the loss of control. To mitigate this, treat your AI agent like a new hire. Use a three-phase approach to build confidence and refine the “loops.”
Phase 1: The Co-Pilot (Human-in-the-Loop)
- How it works: The agent performs the Observe, Reason, and Act steps, but it waits for human approval before executing any changes.
- Goal: Validate the agent’s “Reflection.” You aren’t saving time yet; you’re training the system and identifying where its logic goes wrong.
- Ops Leader Action: Review every action. If the agent makes a mistake, don’t just fix it—adjust the rules or prompts so its next “loop” is smarter.
Phase 2: The Supervisor (Human-on-the-Loop)
- How it works: Once the agent’s accuracy hits a high threshold (e.g., 95%), you allow it to execute low-risk tasks automatically. However, a human reviews the results periodically (e.g., a daily audit of 10% of cases).
- Goal: Scale efficiency while maintaining a safety net. This is where you start seeing real ROI in reduced handle times.
- Ops Leader Action: Focus on “edge cases.” Look for the 5% of interactions where the agent’s reflection failed and refine the escalation triggers.
Phase 3: The Delegate (Human-out-of-the-Loop)
- How it works: The agent handles the entire workflow autonomously. Humans only get involved if the agent hits a predefined “Stop Rule” (e.g., a high-value client or a complex compliance issue).
- Goal: Maximum scale and 24/7 reliability.
- Ops Leader Action: Monitor high-level metrics (CSAT, resolution rate) and manage the exceptions. Your team shifts from “doers” to “auditors” and “complex problem solvers.”
6. The “Pilot in a Week” Checklist
Ready to start? Keep the scope tight to reduce risk.
- Pick One Narrow Workflow: Choose a task with a clear start and end (e.g., “Refunds under $50”).
- Map the Paths: Document the “Happy Path” and the top 3 “Unhappy Paths” (where things usually go wrong).
- Define Stop Rules: Clearly state when the agent must hand off to a human (e.g., if the customer mentions “lawyer” or “fraud”).
- Start at Phase 1: Set the agent to “Draft Only” mode for the first week.
- Set Up the Feedback Loop: Create a simple way for your team to flag agent errors during the review process.
7. What Did I Do?
For the client mentioned in the intro, we didn’t just “turn on” an AI. We built a stateful orchestration system using tools like n8n for workflow management, OpenAI for reasoning, and Airtable as a central ledger to track every transaction’s status.
This approach ensured that if a step failed, the system didn’t crash, it simply paused and waited for a retry or human intervention. We also set up automated daily reporting so Accounting had full visibility without needing to trust a “black box.”
I’ll be breaking down the exact A-to-Z technical stack, the “easy restart” mechanism we built, and how we handled the accounting reports in a follow-up article. Stay tuned for the deep dive.
Let’s Discuss
I’m curious to hear from other Ops leaders: What is the one “linear” process in your team that keeps breaking because it can’t handle ambiguity?
Drop a comment below, I’d love to hear which workflows you think are ready for an agentic loop.
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