Learn how to use autonomous AI agents responsibly inside Scrum — without losing empiricism, accountability, or value delivery.
Agents are already in your Sprint. This course makes sure your team knows what they are doing with them — and who is accountable when they are not.
Scrum with Agentic AI is a hands-on course for Scrum Masters, Agile Coaches, Product Owners, and Developers who want to use AI agents responsibly inside Scrum Teams. Students learn how to design Sprints of hours, coordinate AI agents through an agent harness, run convergence cycles, strengthen the Definition of Done, and produce evidence-backed Increments — without weakening empiricism or team accountability.
The course does not replace Scrum with AI. It shows how Scrum and agents work together: AI compresses the time between hypothesis and evidence; Scrum ensures that evidence leads to learning and adaptation. Used correctly, they reduce the time between idea, evidence and value. Used incorrectly, agents build the wrong thing faster.
"We gave instructions to the agents at the beginning of the Sprint. The Developers were defining the specifications in parallel. We expected everything to come together at the end. It did not."
"The CTO and stakeholders are upset. The tracking screen is missing. The estimated time is wrong. The design looks nothing like what we discussed. What happened?"
"Who was responsible for what the agents built?"
Agentic AI is the #1 strategic technology trend. By 2028, 33% of enterprise software will include autonomous agents — up from less than 1% in 2024.
65% of organizations regularly use generative AI. Fewer than 30% have governance frameworks for AI outputs.
76% of developers use AI tools. 45% say they do not fully trust AI output — yet most have no formal inspection process before it reaches production.
No certified course currently addresses how Scrum Teams work with autonomous agents: guardrails, inspection, accountability when agents produce the Increment.
Six capability levels — from understanding the vocabulary to orchestrating a multi-agent harness inside a Sprint.
Distinguish between a prompt, an AI tool, an agent, a workflow, and a harness — and explain why the distinction matters for accountability
Explain how Transparency, Inspection, and Adaptation apply when autonomous agents are producing the Increment
Use the five Scrum Values as decision guardrails for accepting or rejecting agent output inside a Sprint
Define a Sprint Goal that gives agents direction without prescribing implementation
Assess whether AI-generated output qualifies as an Increment under the team's Definition of Done
Identify accountability boundaries — who owns what, and why "the agent decided" is never an acceptable answer
Give a real agentic AI tool a Sprint Goal with context and constraints, and inspect what it produces
Integrate a pre-built set of specialized agents — UX, coding, testing, security, review — into a coordinated harness inside a Sprint
Every concept includes a real architecture diagram and a FastBite example. Every practice is hands-on, not theoretical.
| Topic | Why it matters |
|---|---|
| Prompt vs. Agent vs. Workflow vs. Harness | Most teams confuse these. Confusing them leads to building the wrong governance for the wrong tool. |
| Scrum Values as AI guardrails | The five values are the team's answer to every AI decision inside a Sprint — not abstract principles, but operational filters. |
| Sprint Goals as agent constraints | An agent without a Sprint Goal optimizes for output. An agent with a Sprint Goal optimizes for value. |
| Human-in-the-Loop — bidirectional | Not just approval at the end. Context provider before the cycle + approver after each convergence cycle. |
| AI-generated evidence vs. verified evidence | Output is not evidence until a human owns it. Test results, security scans, and agent confidence scores are not evidence. |
| Agent guardrails | Who defines them, what they contain, what happens when they are missing. Defined by the team, not by the model. |
| Accountability by Scrum role | Product Owner, Developers, Scrum Master — each with clear accountability. Agents have none. |
| Definition of Done for agent-generated work | What "done" means when an agent built the Increment — including Vibe Coding (Karpathy, 2025) and its risks. |
| Practice | Description |
|---|---|
| Sprint of hours | Running a complete Scrum cycle — Planning, convergence cycles, Review, Retrospective — compressed into hours using agents |
| Convergence cycle design | Code Agent ↔ Test Agent ↔ Security Agent loops with defined handoff and inspection points |
| Guardrail definition workshop | Team exercise to define what agents can and cannot do in their specific context |
| Human-in-the-Loop mapping | Identifying which decisions require human judgment and which can be delegated |
| DoD for Agentic AI | Examples: human inspection completed at each cycle, security scan passed before handoff, no agent-generated data exposed without review |
| Vibe Coding analysis | When describing intent and letting agents generate code is a legitimate accelerator — and when it is a governance failure |
Each of the five Scrum Values applied to a real AI decision inside a Sprint — with the FastBite scenario, and the failure mode when the value is violated.
When an agent produces something that does not serve the Sprint Goal, the team discards it — even if it took hours to generate.
FastBite: Agent builds real-time turn-by-turn navigation. Impressive. Not in the Sprint Goal. Discarded.
The courage to stop a Sprint when AI-generated output passes tests but fails the Definition of Done in a way the tests don't catch.
FastBite: Tracking screen passes all tests. Driver's phone number is visible. "We stop here. This goes back."
Agents generate output beyond the Sprint Backlog. The team's job is to keep agents focused on the Sprint Goal.
FastBite: Agent suggests loyalty points, restaurant rating, promotional banner. All for the backlog. Not this Sprint.
The team is always clear about which decisions were made by a human and which were made by an agent.
FastBite Sprint Review: "The tracking screen was built by an agent and reviewed by the team. The privacy constraint was added after the agent's first version."
AI agents assist human judgment. They do not replace it. Accountability for every decision belongs to a person.
FastBite: Agent recommends 15-minute estimate. Developer accepts without checking. 40% of orders are wrong. "The agent suggested it." — "Who accepted it?"
6 modules · 8 hours · 1 day. Click any module to expand.
FastBite case introduction. What agents are, what they cannot do, and why Scrum is more important — not less — when agents accelerate delivery.
The five Scrum Values applied to real AI decisions inside a Sprint. Each value with a FastBite scenario — what the agent produced, the team's decision, and what happened when the value was applied vs. when it was violated.
Every concept includes a reflection question, theory, architecture diagram, and FastBite example.
How to design a Sprint that runs in hours without breaking Scrum. When agents generate output in hours, the Sprint does not become unnecessary — it becomes shorter.
What "done" means when agents built the Increment. Vibe Coding and its risks. Sprint Review with AI-generated evidence.
Download the pre-built course agent bundle. Give the harness a Sprint Goal. Run a full convergence cycle — UX Agent → Code Agent → Test Agent → Security Agent. Inspect at each handoff. Deliver an Increment. No coding skills required.
The entire Scrum Team — not just Developers. No coding skills required.
Especially useful for teams already using Claude Code, GitHub Copilot Workspace, Cursor, Bolt.new, or Replit Agent — and for any Scrum Team that has asked "who is accountable when the AI built it?" and did not have a clear answer.
Basic understanding of Scrum. Recommended but not required: PSM I knowledge, experience with Product Backlogs and Sprint events, basic familiarity with generative AI tools. No programming experience required.
Agents are fast. That is not the problem — it is the premise.
An agent without a Sprint Goal builds without purpose. An agent without a Sprint Review builds without feedback. An agent without short inspection cycles builds failures at the speed of machines.
Scrum does not slow agents down. Scrum is what makes their speed safe to use.
AI should reduce the time between idea, evidence and learning — not eliminate the stops that make learning possible.
One day. Eight hours. The framework your team needs to work with agents without losing accountability.
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