AB-620 study plan
AB-620 · Give each week a clear focus.
8 weeks · About 7 hours per week
Build and configure AI agents and workflows
Map the full lifecycle of an agent (instructions, tools, triggers, channels) and diagram it
Build a minimal working agent that answers from a single connected source
Define at least three tool/action capabilities and wire one into the agent
Log what configuration choices changed agent behavior and why
Your checkpoint
A working single-source agent plus a one-page architecture diagram and behavior-change log
Build and configure AI agents and workflows & Plan and design AI solutions
Design a multi-step workflow with branching logic and at least one human-approval checkpoint
Translate three business requirements into agent configuration decisions
Document where design constraints (latency, cost, data access) force trade-offs
Your checkpoint
A workflow spec with branch diagram and a requirements-to-configuration traceability table
Plan and design AI solutions
Compare three solution patterns (single agent, orchestrated workflow, retrieval-augmented) against a stated scenario
Write design decisions that justify source selection and data-flow boundaries
Identify failure modes for each pattern and propose a mitigation for each
Your checkpoint
A design decision record covering pattern choice, risks, and mitigations for one scenario
Integrate AI solutions with data and services
Connect the agent to two distinct data sources and normalize their outputs
Implement a retrieval step that returns grounded citations
Handle an API error and a timeout with explicit fallback behavior
Document connector authentication and permission requirements
Your checkpoint
An integrated solution reading from two sources with citations and demonstrated error fallback
Integrate AI solutions with data and services & Test, monitor, and optimize AI solutions
Build a 15-case evaluation set spanning happy path, edge cases, and adversarial inputs
Run the evaluation and record pass/fail with reasons
Instrument logging for inputs, tool calls, and outputs
Tune at least one configuration parameter and measure the change
Your checkpoint
An evaluation report with baseline scores, a tuning change, and post-change scores
Test, monitor, and optimize AI solutions
Define three monitoring signals (quality, latency, cost) and set alert thresholds
Create a regression test that catches a previously observed failure
Analyze one cost driver and reduce it without dropping below quality bar
Your checkpoint
A monitoring dashboard outline plus a passing regression suite and a cost-reduction note
Secure, govern, and deploy AI solutions
Apply least-privilege access to agent identity and connected data
Classify data handled by the solution and document handling rules per class
Configure content safety and prompt-injection guards, then test them
Draft an audit trail requirement list
Your checkpoint
A security and governance checklist completed against the built solution, with guard test results
Secure, govern, and deploy AI solutions & Test, monitor, and optimize AI solutions & Plan and design AI solutions
Package the solution for deployment with environment separation
Run a full end-to-end validation in a pre-production environment
Re-run the evaluation set and confirm no regression
Write a go-live and rollback runbook
Your checkpoint
A deployed pre-production solution with final evaluation scores and a go-live/rollback runbook
Milestones
Week 2: Working agent and a multi-step workflow both function end to end without manual intervention
Week 4: Solution retrieves grounded answers from two sources and survives an injected API failure
Week 6: Evaluation suite passes at an agreed threshold and monitoring signals are defined with thresholds
Week 8: Pre-production validation passes with no regression and a complete runbook exists