Agentic AI Webinar 20260713
Panel briefing · Getting started with agentic AI
Automation follows the path.
Agentic AI decides it.
Eight straight answers for a webinar panel on agentic AI — written for a room with mixed technical backgrounds, real budgets, and no patience left for hype.
Automation
Same steps, same result, every time.
Agentic AI
Chooses a path, based on what it sees.
Every question below comes back to this one difference. Here's the short version, before the long one.
Every one of these questions comes up on stage the moment "agentic AI" enters the conversation, in more or less this order: what is it, how is it different, are we ready, do we even need it, how do you supervise it, why do pilots stall, how do you shop for it, and what do you do Monday morning. This briefing answers all eight in plain language, without leaning on any one vendor's story.
The examples below reference Claude, Gemini, and Microsoft Copilot — the major assistant platforms most people in the room have already touched — alongside open-source tools like LangChain, n8n, and AutoGen for anyone building this in-house rather than buying it.
Q1 · Definition & example
Give me the plain-English definition of agentic AI, and an example of how it works.
Agentic AI is software that decides its own next step toward a goal, instead of waiting for a person to tell it what to do next.
Most AI people have used so far — a chatbot, a writing assistant, a search summary — answers one question and stops. You ask, it responds, you decide what happens next. Agentic AI closes that loop: you give it a goal and access to a set of tools, and it plans a sequence of actions, checks what happened after each one, and adjusts, without a person approving every step in between.
Say a customer emails asking to move a delivery date. A standard AI assistant drafts a polite reply for someone to review and send. An agentic system reads the inbox, checks the calendar and the order system, picks one of the open delivery windows that actually works, updates the order, and sends the confirmation — the whole chain unsupervised, because it was given the goal ("resolve the reschedule request") rather than the single task ("write a reply").
Claude and Gemini both support this pattern today through what's usually called tool use — the model calling real systems instead of just generating text. Microsoft Copilot Studio, and open-source frameworks like LangChain or n8n, let you wire the same pattern together without building it from scratch.
Q2 · Automation vs. agentic
What is the difference between something that is automated and something that is the product of agentic AI?
Automation follows a path you already drew. Agentic AI draws the path itself, case by case.
If you can map an entire process as a flowchart — every box, every branch, every condition already known before a single case runs through it — that's automation, however sophisticated the tooling. It runs the same way every time it meets the same conditions, and it has no way to handle a case nobody anticipated; it just stops, errors, or routes to a human.
Agentic AI is doing genuine judgment work at each branch: given this specific situation, what's the sensible next move? It can handle the case nobody wrote a rule for, because it isn't following a rule — it's evaluating the situation the way a person would. That's the trade: automation is cheap, predictable, and dumb; agentic AI is a judgment call, which is more capable and much harder to fully predict in advance.
The practical tell, when someone tells you their product is agentic: ask what happens on the case in twenty that doesn't fit the happy path. If the honest answer is "the rule doesn't cover that, so it fails," you're looking at automation with a chat interface on top of it, not agency.
Q3 · Readiness checklist
What has to be true about your operation before you start? What does an agent need from you on day one that most operations don't have ready?
An agent needs the operational discipline you'd want around any new hire with real authority — and most shops have never had to write that discipline down, because a person just used judgment.
Before you turn an agent loose on anything real, get five things in place:
- A named owner. One person accountable for what the agent does — not "the team," an actual name. If something goes wrong, someone's job has to be to answer for it.
- Data it's actually allowed to touch. Most operations don't know, with confidence, what's in their own systems or who currently has access to what. An agent will happily use everything it can reach, so you need to know what that is before you turn it on, not after.
- A written boundary. What can it decide on its own, and what dollar amount, scope, or action always requires a human? Write the limit down before the agent needs it, not after it crosses one.
- A record of every decision. What it saw, what it decided, and why — logged automatically from day one, not bolted on later once something goes wrong.
- A fast, tested way to turn it off. Not "we could probably disable it" — an actual switch someone has practiced using.
None of this requires the agent itself to build it, and none of it is exotic. It's the operational hygiene you'd already want around anyone with real authority to act on your behalf — most operations just never had to make it explicit before, because the person doing the job was accountable by default.
Q4 · Do you actually need it
Does the average shop actually need agentic AI, or is well-built automation ninety percent of the value at a fraction of the risk?
For most operations: well-built automation and AI-assisted tools capture the bulk of the value, at a fraction of the oversight cost. Agentic AI earns its place in a narrower set of cases.
Agentic AI is worth its overhead specifically when three things are all true: the decision happens too often for a person to touch each one, the right answer genuinely varies case by case in a way that can't be pre-written as a flowchart, and the judgment call is worth more than the cost of the guardrails it requires — the ownership, boundaries, and logging from the last question. That's a real slice of most operations, but it's a narrow one.
Everything else — routing, scheduling, drafting, summarizing, triage, internal search — is better served by automation or an AI-assisted tool where a person still makes the final call. It's cheaper to build, far easier to test, and doesn't need a kill switch, because a human is already in the loop.
The honest sequencing: build the automation first. In practice, it will surface exactly where the process keeps needing a judgment call automation can't make, and that's the one place agentic AI is actually worth the investment. Starting with agentic AI everywhere is usually solving a problem you don't have yet, at the cost of one you do.
Q5 · Supervision & QA
How do you supervise something that makes its own decisions? What does QA even look like when there's no script to score against?
You stop grading it against a script and start auditing its judgment — the way you'd evaluate a person over time, not a piece of software once before shipping.
Traditional QA checks whether a system followed the steps. An agent doesn't have one set of steps, so the questions change:
- Did it stay inside its boundaries? Every action should be checkable against the limits you wrote down in question three.
- Can you reconstruct why, after the fact? Pull the log for one transaction and see if the reasoning holds up. If you can't reconstruct it, you don't have oversight — you have a black box with a dashboard.
- Does it match a person's judgment on a sample of real cases, reviewed on an ongoing basis, not once at launch? Judgment quality drifts as conditions change; QA has to be continuous to catch it.
- Have you tried to break it on purpose? Feed it edge cases and bad-faith inputs before a real customer does, and see whether it escalates gracefully or improvises something nobody authorized.
- Are outcomes even across the people it deals with? An agent can be accurate on average and still be quietly unfair to a subgroup — check for that specifically, not just overall accuracy.
- Is it escalating the right things? Track the ratio of handled-independently to sent-to-a-human, and watch which direction it's moving. Both a rising and a falling trend can be a warning sign, depending on why.
This looks much more like supervising a new employee's judgment over their first year than testing software before a release — and it doesn't end at launch.
Q6 · Where pilots die
Where do these pilots die? Not the technology — the organizational spot where agentic projects quietly stall and never reach production.
Almost never in the demo. Almost always at the moment someone has to put their name on the outcome.
The most common death is right at the handoff from "impressive pilot" to "who is accountable when this is wrong, with real money or a real customer on the line." As long as it's a sandbox, everyone's enthusiastic. The moment it touches production, the named-owner question everyone skipped as premature during the pilot becomes the thing nobody wants to answer, and the project quietly stops moving.
The second most common spot: legal or compliance gets looped in late, discovers nobody documented what data the agent touches or where its decision boundaries are, and the project sits in a review queue that should have started on day one, not after the pilot succeeded.
The third: the pilot ran against a clean, cooperative slice of volume, and it can't actually handle the messy fifteen-to-twenty percent of real cases — bad data, ambiguous intent, conditions nobody anticipated. Nobody budgeted time for the exception path, so the pilot never scales past the group it was tested on. It just stays a pilot.
All three are organizational, not technical. The fix is the same in every case: answer the ownership, data, and exception-handling questions before the pilot starts, not after it works.
Q7 · Vendor questions
Every vendor now has "agentic" on the website. What two or three questions separate the real thing from a chatbot in a trench coat?
Ask to see it act, not talk. Ask what happens when it's confused. Ask for the log and the off switch.
"Show me it taking an action, not producing a suggestion." A lot of "agentic" products still hand everything to a person to approve before anything actually happens — a good, safe design in plenty of cases, but not agency. Ask them to demonstrate the system completing something end to end, with no human in the middle of that specific run.
"Walk me through what happens on a case it wasn't built for." A real agentic system has a defined answer: it escalates, it asks a clarifying question, it stops safely. A relabeled chatbot either fails silently or produces a confident, plausible-sounding action that's wrong. Ask specifically to see that failure path, not just the happy path.
"Show me the decision log for one case, and show me the off switch." If they can't hand you a readable record of what the system saw and why it acted, or there's no fast way to shut it down, it wasn't built for autonomy — no matter what the homepage says.
If a vendor answers all three cleanly and specifically, you're likely looking at the real thing. If any answer goes vague or turns into a demo of chat quality, you're looking at a well-dressed chatbot.
Q8 · Start this week
For someone who wants to start tomorrow morning — what's the one move this week that costs nothing and gets them on the path?
Before you touch a vendor or a platform, write down — in plain language — one decision a person in your operation makes every day, and how they actually make it.
Pick one recurring decision: which delivery slot to offer, how to route a request, when to approve an exception. Sit with the person who makes that call and write down, precisely, what they look at and how they decide — not the policy-manual version, the actual version.
This costs nothing but a conversation and an hour of writing, and it does two things at once. First, it tells you honestly whether that decision is a fit for agentic AI at all — if you can sketch it as a flowchart in ten minutes, it's automation, not agentic, and you've just saved yourself the harder build. Second, if it can't be reduced to a flowchart, that document becomes the actual spec: the thing you hand to Claude, Gemini, Copilot, or any other platform later, instead of a vague description of what you want.
Most agentic projects don't stall because the technology can't do it. They stall because nobody in the building could describe the decision precisely enough for anyone — human or machine — to make it consistently. That gap is free to close, and closing it is the real head start.
Glossary
- Agentic AI
- Software that plans and takes a sequence of actions toward a goal on its own, adjusting as it goes.
- Automation
- A fixed, pre-defined path that runs the same way every time the same conditions occur.
- Action boundary
- The explicit limit — dollar amount, scope, or action — an agent cannot cross without a human.
- Named ownership
- One accountable person responsible for what an agent does, by name, not by team.
- Kill switch
- A tested, fast way to stop an agent's actions immediately.
- Escalation path
- Where an agent sends a case it isn't confident handling, and to whom.
- Tool use / function calling
- The mechanism that lets an AI model operate real systems — calendars, databases, payment tools — instead of only producing text.
- Disparate impact
- When an otherwise-accurate system produces systematically worse outcomes for one group of people than another.
- Adversarial testing
- Deliberately trying to break a system with edge cases and bad-faith inputs before real users do.
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