See the structure, then implement and verify with the notes.
Trace a completion claim to versions, actual traces, tests and inputs. Screenshots illustrate; they do not replace reproducibility.
What situation are you walking into?
The committee wants more than a successful demo: what if data is wrong, why not rules, what did you build, and what happens if business impact is inconclusive?
01 / Hand the project to another person
Document setup, installation, data, run commands, tests and limitations for a fresh environment. Provide example config without secrets and synthetic data without customer records. Demonstrate normal work, missing evidence, denied access and recovery. Video supplements reproducible evidence.
02 / Defend why, not only what
Use fifteen minutes for problem, baseline, decisions, boundaries, demonstration, results, limits and reuse, followed by fifteen minutes of challenge. Link each material claim to evidence. A technology list cannot replace a rationale; more agents do not automatically improve a system.
03 / Assess competence, not certificate theater
Assess diagnosis, semantics, engineering, evaluation and safety, value evidence, and collaboration and reuse. Treat secrets, fabricated evidence, unauthorized actions, non-runnable work or mislabeled simulation as blockers. Site self-checks are learning feedback, not accreditation or employment guarantees.
04 / Make growth and portfolio claims verifiable
Repeat the initial self-assessment and support changes with artifacts. Document context, your contribution, decisions, evidence, results and failures. Practice coding, system design, decomposition and customer communication without presenting a universal interview sequence.
Put the method to work
Credible portfolio wording: implemented identity mapping, metric contracts and controlled tools on synthetic retail data with scope, duplicate and recovery tests; planning is simulated and model-driven traces remain to be added. This is stronger than fabricating a customer growth story.
Practice this week
- Submit prior deliverables, final code and an evidence index.
- Demonstrate normal work, failures and a new constraint.
- Complete reassessment, portfolio description and a thirty-day plan.
Acceptance checks
- Reproducible in a fresh environment, with honest scope.
- Decisions trace to evidence.
- Failures can be diagnosed, stopped or recovered.
120 minutes: defend delivery under an unseen constraint
A defense is more than a recording. Reviewers introduce late data, a timeout, reduced permissions or a changed definition. Explain the impact before demonstrating reproducible evidence and a safe stop.
0–30 min: freeze the portfolio version and start from a clean environment. Link each capability claim to a file, test, trace or review.
30–90 min: run two 30-minute defenses: problem (5), working slice (10), failure (10), value (5). Rotate builder and reviewer roles.
90–120 min: reassess on a comparable unseen task. Record personal contribution, collaboration needs and the next real validation.
Artifact excerpt · synthetic example
Portfolio index
01-fit/: value, boundary, unknowns
02-data/: synthetic/authorized data, contracts, recalculation
03-system/: tools, model integration, versions
04-evals/: failures, regression, actual traces or simulation labels
05-ops/: runbook, costs, stop and recovery
06-value/: acceptance, attribution limits, next action
07-reuse/: rights, reusable assets, transfer testsInstructor review
Unimplemented work can be acknowledged; ambiguous implementation claims cannot. Review role-appropriate evidence. Reading checkmarks are not assessment or external accreditation.
An unseen field challenge ↗
Answer: what business judgment changed, how does the system bound harmful action, and what lets the next owner continue operating it?
A design, deterministic simulation, model integration and production operation require different evidence. Label the level actually reached.
All offline simulator tests pass. Has real model capability been validated?
Mechanism tests and model capability evaluations are different evidence classes.
References and further reading
Anthropic ↗ · Demystifying evals for AI agents2026-01-09 · AnthropicAnthropic ↗ · Building effective agents2024-12-19 · AnthropicRead alongside the assignment and record design constraints and tests. Verify specifications against your deployment version.