Learning tracks by role

AI learning track: Engineers taking AI to production

For engineers and architects responsible for operating AI systems beyond a working demo.

After this track: You can define release checks, trace failures and bound the cost and authority of a production agent.

Follow these 10 steps in order, or return to the one you need for your current work. Reading times are estimates; listening times cover the full episode.

  1. How do you evaluate an AI agent?

    Explainer · About 3 min reading

    Start with release checks that cover tool choices and task completion as well as the final answer.

  2. What's the difference between AI observability, evaluation, and benchmarking?

    Explainer · About 1 min reading

    Separate traces, quality judgments and comparisons before deciding what your monitoring stack must measure.

  3. What are AI agent guardrails, and how do you set them?

    Explainer · About 1 min reading

    Turn known failure modes into runtime boundaries before exposing the agent to production traffic.

  4. How do you cut the cost of running an AI agent?

    Explainer · About 5 min reading

    Use traces to find expensive calls and retries once quality checks can catch regressions from cost changes.

  5. Latency

    Glossary · About 1 min reading

    Define the response-time constraint alongside cost before optimizing a workflow users still have to wait for.

  6. Why do multi-agent systems fail, and how do you make them reliable?

    Explainer · About 1 min reading

    Inspect coordination and error propagation before splitting a working agent across more participants.

  7. What are the types of AI agent memory?

    Explainer · About 3 min reading

    Decide what should persist across runs after identifying the state that failures can corrupt or make stale.

  8. How do you give an AI agent an identity and permissions?

    Explainer · About 6 min reading

    Give each acting component an identity and bounded permissions before connecting it to privileged services.

  9. Architecting Reliable Agentic AI | Cisco’s Giovanna Carofiglio on the AGNTCY Collective

    Episode · About 41 min listening · full episode

    Compare your architecture with a discussion of agent interoperability and reliability before expanding across systems.

  10. The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn

    Episode · About 44 min listening · full episode

    Close with the trust and reliability tradeoffs you will need to explain when deciding whether to ship.

Choose another role or continue through the five-level pathway.