GitHub Certified: Agentic AI Developer study plan

Map the full agent lifecycle in a GitHub-driven SDLC and label each hand-off point where a human must supervise

Write an explicit supervision contract for one agent: scope, permitted actions, escalation triggers, and stop conditions

Configure branch protection and required-review rules that prevent an agent from merging to a protected branch unreviewed

Draft three realistic failure scenarios (runaway loop, over-broad edit, silent test skip) and the operator response to each

Define the agent's tool and permission surface and justify each permission against a required capability

Author the agent instruction file that specifies task intake, planning steps, verification steps, and reporting format

Simulate three supervised task runs by hand and log every point where the agent needed a human decision

Revise the instruction file to eliminate at least two of the logged decision points

Configure an agentic system end to end: triggers, context sources, tool access, output destinations, and secrets handling

Separate read-only and write-capable tool access into distinct configurations and verify the restricted one blocks writes

Set context-window and file-scope limits, then confirm the agent refuses an out-of-scope request

Document every configuration parameter and its default, owner, and change-approval path

Build a scored evaluation rubric for agent output covering correctness, scope discipline, test coverage, and explanation quality

Assemble a fixed evaluation set of at least ten representative tasks with expected outcomes

Run the evaluation set against the configured agent and record scores per criterion

Identify the three lowest-scoring criteria and trace each to a specific configuration or instruction cause

Automate the evaluation set so it runs on every change to agent configuration

Define pass/fail gates that block a configuration change from promotion when evaluation scores regress

Add regression cases for the failures found in week 4 and confirm the gates now catch them

Compare agent output quality across two model or prompt variants and select one with stated evidence

Orchestrate two agents on a shared task with a defined hand-off contract and artifact format

Implement concurrency and conflict handling so parallel agent work does not collide on the same files

Define retry, timeout, and rollback behavior for a failed agent run and demonstrate each triggering correctly

Instrument the pipeline with logs and metrics that let an operator reconstruct what an agent did after the fact

Enumerate the risk surface of the orchestrated system and assign each risk a mitigation, owner, and detection method

Enforce least-privilege credentials and verify that an agent cannot reach resources outside its declared scope

Write the audit trail specification required to answer who approved what, when, and on what evidence

Run one adversarial or malformed-input scenario and confirm the system fails safely rather than silently

Rebuild the full agentic workflow from scratch in a clean repository without consulting prior notes

Re-run the complete evaluation suite and compare scores against the week 4 baseline

Close every gap revealed by the rebuild and re-verify the affected configuration and governance controls

Produce a one-page operational handover covering supervision, evaluation gates, orchestration behavior, and safety controls