PRIMARY SOURCES
Read the originals
Wherever the course makes a framework-specific or fast-changing claim, it cites a primary source. Modules link one to three of these in place; this page is the full annotated list. No link dumps: each entry says why it is worth your time.
Framework and hosting pages change quickly. These links are reviewed before each course revision, and the repo pins the exact dependency versions the course was tested against.
LangGraph and Deep Agents
- LangGraph persistence
- The authoritative answer to what one checkpoint contains and which tables the Postgres saver writes. Read next to your own checkpointer from Module 03.
- LangGraph interrupts
- Exact semantics of interrupt() and Command(resume=...), including what replays on resume. The replay behavior is the part that surprises people in Module 07.
- LangGraph graph API
- Where the docs themselves tell you to keep node logic idempotent, which is the bridge into Module 04.
- Deep Agents overview
- The harness Module 12 interrogates. Read the mechanisms, then map each to the module where you built it by hand.
- Deep Agents context engineering
- Module 08's three techniques as framework features.
- Deep Agents subagents
- Clean-context subagent spawning, the deep-dive pattern formalized.
- Deep Agents going to production
- Compare its defaults against your own security_policy.md from Module 10.
Long-running harness engineering
- Anthropic: effective harnesses for long-running agents
- The industry statement of the harness/runtime split this course is built around.
- Anthropic: effective context engineering for AI agents
- The published basis for Module 08's claim that compaction alone is not sufficient.
- Anthropic: harness design for long-running apps
- Harness design decisions for multi-session application development.
- Anthropic: scaling managed agents
- What running other people's agents as a service forces you to make durable.
- Anthropic: multi-agent research system
- A production long-horizon system described with unusual candor; Module 13's first extraction case.
Evals
- Hamel Husain: LLM-as-a-judge
- The methodology Module 11 follows, including judge validation against human labels. If you read one link on this page, read this.
- Hamel Husain and Shreya Shankar: evals FAQ
- Short answers to the exact objections your team will raise when you propose error analysis before metrics.
Durable workflow comparison
- Temporal AI integrations
- The landscape when a workflow engine owns durability instead of your tables.
- Temporal durable OpenAI Agents example
- Claim, lease, heartbeat and reclaim as engine primitives; compare with your Module 05 scheduler.
- Temporal human-in-the-loop example
- The durable-wait pattern of Module 07 in a workflow engine, showing it is an industry invariant and not a framework quirk.
Current industry examples
- OpenAI: harness engineering
- The coding-agent harness architecture from the team shipping it.
- OpenAI: unrolling the Codex agent loop
- The agent loop of Module 01, at production scale.
- OpenAI: run long-horizon tasks with Codex
- Long-horizon task guidance for a shipping agent product.
- Replit: evaluating and improving Agent at scale
- Evals as the development loop, argued by someone with millions of runs.
- Replit: decision-time guidance
- Steering an agent mid-trajectory rather than only at the prompt.
- Replit: inside the snapshot engine
- Durable execution machinery from a production agent platform.
Hosting limits
- Render free instance limitations
- The constraints Module 14 designs around. Verify before relying on the module's numbers.
- Neon free-plan limits
- Connection and storage caps that shape the lease scheduler and pooling choices.