AgenticEdBack to course01 · Build
Make the system
Turn a real goal into an agent workflow that can be inspected and improved.
- Define the user and task
- Map Think, Act, and Observe
- Choose tools and data sources
- Build a working prototype
AgenticEd is organized around one rule: an agent is not finished when it works. It is finished when its purpose, risks, oversight, performance, and limitations are visible to another person.
The three-stage method
02 · Govern
Bound the system
Make responsibility and failure handling part of the design, not an afterthought.
- Identify harms and failure modes
- Protect data and source quality
- Add human approval points
- Define escalation and ownership
03 · Prove
Test the claim
Replace “it seems useful” with repeatable tests, artifacts, and honest limitations.
- Create representative test cases
- Measure quality and reliability
- Record failures and revisions
- Publish an evidence portfolio
Every project follows: Goal → Build → Govern → Test → Reflect → Revise. The cycle repeats until the learner can explain both what the agent does and when a human must intervene.
Reference implementation: Research Agent
To make the method practical, AgenticEd uses one complete project as its reference implementation. The learner builds a research agent that summarizes public sources, labels claims, refuses unsupported or high-stakes requests, and requires human verification before reuse.
Complete the project: define the research question, paste the tested system prompt, run five deliberate failure tests, add one governance fix, rerun the failed test, and submit the evidence portfolio.
From concepts to agent evidence
AgenticEd's independent framework turns AI concepts into a practical project cycle. Learners do not stop at understanding how agents work; they build one, govern it, and produce evidence that another person can inspect.
AgenticEd concept
What the learner understands
AgenticEd learner action
What the learner does in a project
Evidence produced
What another person can inspect
Perception
What the system receives and how it interprets the environment.
Build: define inputs and goal
Specify the user, task, inputs, assumptions, and what the agent is allowed to observe.
Input and scope record
Problem statement, data sources, permissions, and out-of-scope uses.
Learning
How systems improve from data, feedback, or experience.
Prove: test, observe, revise
Compare expected and observed outputs, record failures, and explain each revision.
Test and revision log
Representative cases, failure examples, results, and changes made.
Natural interaction
How people communicate with AI systems and how systems respond.
Govern: design human control
Define approval points, explanations, escalation, refusal behavior, and responsibility.
Responsibility map
Human owner, user-facing limits, intervention rules, and risk mitigations.
Framework note: These concepts are organized by AgenticEd around the agent lifecycle, with responsible-AI judgment and evidence required in every project.
What makes this framework distinct
Accountability is structural
The learner maps ownership for decisions, data, tool calls, and failures.
Trust is earned with evidence
Projects include tests, observed failures, revisions, and limits, not just a demo.
Governance is beginner-accessible
No-code and offline paths teach the same judgment as technical implementations.
Artifacts compound
One artifact per stage becomes a portfolio that shows reasoning, not only completion.
Required learner evidence
An Agent Evidence Portfolio contains
- Problem statement, intended user, and success definition
- Agent workflow showing inputs, decisions, tools, and outputs
- Responsibility map identifying human owners and approval points
- Risk register with likely failures and mitigations
- Test cases, results, and at least one documented failure
- Revision log explaining what changed and why
- Trust Readiness Rubric score with limitations and next steps
Framework status: This is a public working specification. Educators are invited to test it, report outcomes, and propose revisions. Adoption and results should be documented rather than assumed.