Why this hour is worth your calendar
Agents are now a board topic. Budgets are being approved, vendors are pitching, and analysts are warning about cancellations - all using the same word to mean five different things. Gartner has a name for the worst of it: agent-washing, vendors relabeling ordinary chatbots and automation as "agents". Nobody on your data team can do that due diligence for you in a board meeting or a vendor call. That makes the vocabulary, and the skepticism, YOUR job. This track exists so you can do it in plain English, with zero code, ever.
Three machines that look alike 9 min live
A chatbot, an automated workflow, and an agent can produce identical-looking demos. Under the hood they are different machines with different costs, different failure modes, and different governance needs. The ladder below is the whole session on one picture.
LiveChatbot, workflow, or agent - definitions that hold up3 min▶
Anthropic's engineering team drew the line the industry now uses, and it fits in two sentences. A workflow is a fixed flowchart: YOU decided the steps in advance, and the model fills in text at some of them. An agent is a system where the MODEL chooses the steps - it decides what to look up, what to do next, and when it is done.
- Chatbot: answers when spoken to. One question in, one answer out. Useful, cheap, and not an agent.
- Workflow: "summarize the ticket, classify it, route it" - three fixed steps, forever, with a model inside some of them. Predictable and auditable.
- Agent: "resolve this ticket" - the model decides whether to search the knowledge base, check the order history, escalate, or answer. Flexible, and much harder to predict.
Notice what the distinction is NOT about: model quality, vendor, or how impressive the demo looks. It is about one thing - who chooses the steps.
LiveRPA vs agents - and the 2026 hybrid pattern3 min▶
Your organization probably already runs automation: RPA - robotic process automation - the software robots that click through screens and move data between systems. RPA is a fixed script: brilliant at doing the exact same thing ten thousand times, brittle the moment anything changes. Agents are the opposite: they reason toward an outcome, so they handle variation - and introduce unpredictability.
- RPA: deterministic, cheap per run, fully auditable, breaks on exceptions.
- Agents: flexible on exceptions, costlier per run, harder to audit, can surprise you.
- The 2026 hybrid pattern: keep RPA on the deterministic 70% of a process, and point agents only at the exception-heavy 30% that used to land in a human queue. Not either/or - a division of labor.
The claims team that stopped arguing. An insurance operations group (anonymized) spent a quarter debating "RPA vs agents" for claims intake. The answer was the split: RPA kept the standard claims it already handled flawlessly, and a pilot agent took only the malformed, multi-document exceptions. The debate was never RPA or agents - it was where to draw the line between them.
LiveAgent-washing - the due-diligence trap3 min▶
Gartner has warned loudly about agent-washing: vendors relabeling existing chatbots, RPA suites, and scripted assistants as "AI agents" because the word now opens budgets. The product did not change; the label did.
- Why it matters to you: agent pricing on chatbot capability means you overpay for the label; agent risk controls on a chatbot means you over-govern it - and agent claims with no agent architecture mean the roadmap you were sold does not exist.
- The one-question test: "does the model choose the steps at runtime, or did your engineers fix the flow in advance?" Vendors selling relabeled workflows struggle with that question in the room.
- The trap: the demo cannot tell you. A polished fixed workflow demos exactly like an agent. Only the architecture answer - and a trace of a real run - can tell you. Exercise 2 gives you the full five-question screen.
The only rule that saves money 8 min live
Every rung up the ladder buys flexibility and costs money, speed, and auditability. The organizations shipping successful AI in 2026 share one habit: they start at the bottom of the ladder and climb only under evidence. Here is the whole discipline as a two-question flow.
LiveStart simple - the rule with a pedigree3 min▶
Anthropic's advice to its own customers is disarmingly modest: a single LLM call is usually enough. Summarize this, draft that, classify these - one ask, one answer, no ladder-climbing required. Their standing rule: add complexity only when it demonstrably improves outcomes.
- "Demonstrably" is the load-bearing word. It means measured, not felt. A demo is not a demonstration of improved outcomes; a comparison against the simpler version is.
- As a leader, this hands you a wonderfully polite challenge for any proposal: "what did the simpler version score, and by how much does this beat it?" If nobody built the simpler version, the conversation is already valuable.
LiveMcKinsey's two lessons from 50+ builds3 min▶
McKinsey teams distilled six lessons from more than fifty agentic builds. Two of them do most of the work for a leader, and both are pleasingly deflationary:
- "It's not about the agent, it's about the workflow." The value never comes from the agent as an artifact - it comes from redesigning the business workflow around it. Teams that bolt an agent onto an unchanged process ship a demo; teams that redesign the process ship savings.
- "Agents aren't always the answer." From the people building them for a living. Low-variance, standardized processes are better served by rules, RPA, or fixed workflows - lower cost, higher auditability, no surprises.
Together they hand you the questions for any internal pitch: which workflow is being redesigned, and why is an agent the right rung for it? A proposal that cannot answer both is a technology looking for a problem.
The pitch that answered both. A logistics team (anonymized) proposed an "exceptions agent" and opened with the workflow slide, not the agent slide: here is the process today, here is the redesigned process, here are the two steps where variance genuinely requires model-directed decisions. Approval took one meeting. The pitch before it - "we want to build an agent" - had taken four and gone nowhere.
Self-studyCost intuition - what each rung multiplies2 min read▶
You do not need to price tokens to have the right instinct. Each rung up the ladder multiplies three things at once - and the multiplication compounds:
| Rung | What multiplies | Leadership read |
|---|---|---|
| Single call | Baseline: one model call per task | Cheap enough to try on anything |
| Workflow | A few calls, fixed and known in advance | Costs are predictable line items |
| Agent | Unknown number of calls + tool runs per task | Budget becomes a range, not a number |
| Multi-agent | Agents calling agents; failure modes multiply too | Needs spend caps and monitoring by design |
Tokens, latency, and failure modes all climb together. The runaway-cost stories in session a3 are what happens when this table is discovered in production instead of in planning.
Sort your own portfolio ★ 12 min · pen and paper
The ladder becomes yours the moment you sort real work onto it. No code - a pen, the decision flow above, and eight honest minutes.
List 8 real tasks from your organization that someone has proposed (or you suspect someone will propose) "putting AI on". Mix sizes: a report, a customer reply, a reconciliation, an analysis.
Run each through the two-question flow: steps predictable? → workflow. High variance AND tools needed? → agent. Neither? → single call. Write the rung next to each task.
Count the rungs. In most honest portfolios the majority land on single call or workflow - if all eight landed on "agent", re-run the first question with a colder eye.
Circle the one task you (or your organization) would have over-engineered - the workflow that was about to be funded as an agent. That circle is today's return on 45 minutes.
The agent-washing screen ★ 8 min · your next vendor meeting
Five questions that separate an agent from a relabeled chatbot - politely, in one meeting. Practice them out loud once; they land differently when they sound routine.
"Does the model choose the steps at runtime, or is the flow fixed in advance?" The definitional question. Hesitation here tells you most of what you need.
"What happens on an exception it has never seen?" Fixed scripts break or escalate; agents reason. The answer reveals the architecture.
"Show me a trace of one real production run - every step, every tool call." Real agent products produce traces on demand. "We'll follow up" is a data point.
"What limits what it can do and spend - and what happens at the limit?" A real agent vendor has thought hard about guardrails and caps, because their other customers made them.
"Which parts of the product are deterministic automation, and which are model-directed?" The honest answer is almost always "a mix" - and an honest mix beats a pure-agent claim every time.
Before session a2 ◐ 30 min total
- Finish sorting all 8 portfolio tasks if you ran out of time, and keep the sheet - sessions a5 and a6 reuse it for investment and ROI decisions.
- Run the sorting-partner prompt on the two tasks you were least sure about, and notice where the AI pushes back on your rung choice.
- Use at least two of the five agent-washing questions in a real conversation this week - a vendor call, a team review, or a pitch you receive.
- Optional reading: Anthropic's essay "Building effective agents" - you now have the ladder, so it reads as a friendly confirmation rather than a technical paper.
- Of everything we currently call an "agent", where does each one actually sit on the autonomy ladder?
- Which of our processes are predictable enough that a workflow or RPA would be cheaper and more auditable than an agent?
- Where have we added agent complexity without evidence that it demonstrably improves outcomes over the simpler version?
- If a vendor pitched us "agents" tomorrow, who runs our due-diligence screen, and what is on it?
- What is the simplest version of our most ambitious AI idea - and have we tried it first?
Official sources covered
The leader track teaches from published engineering guidance, consulting research, and analyst warnings - no vendor marketing. This page covers:
Three questions before you go 🎯 ◐ 90 seconds
1 · Anthropic's line between a workflow and an agent is...
Who chooses the steps - that is the whole distinction, and it decides cost, auditability, and governance. Not model quality, not vendor, not the demo.
2 · A vendor demo looks impressively agentic. The single best due-diligence move is...
A polished fixed workflow demos exactly like an agent - only the architecture answer and a real trace can tell them apart. That is the heart of the agent-washing screen.
3 · Your team proposes a multi-agent system for a process with the same four steps every time. You should...
Predictable steps = workflow territory. McKinsey's builders say it themselves: agents aren't always the answer, and each rung up multiplies cost and failure modes.