Courses / Hard Deployments / Autonomy over long horizons

Lesson 18 of 36 · PremiumCourse syllabus

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1. Serving on hardware you did not choose

Budget weights, resident cache and runtime memory in consistent units30 min · Free
Measure throughput and user latency before choosing a batching policy30 min · Free with sign-in
Hardware arrives on a procurement cycle and your estimate arrives in a meeting30 min · Premium
Measure throughput without losing the requests that failed180 min · Premium
Build and verify an inference service under failure240 min · Premium

2. The air gap

Find the dependencies your offline build and first request still need30 min · Free with sign-in
Make recovery work with the releases already inside the boundary30 min · Premium
Make evidence verifiable and keep the gaps visible30 min · Premium

3. One platform, many customers

Specify and test tenant isolation at every layer30 min · Free with sign-in
Bound shared demand and measure the tenants hidden by the median30 min · Premium
Calculate failure scope from dependencies and rollout state30 min · Premium
Test the model server's cache, adapter and shared-failure boundaries30 min · Premium

4. Cost and latency across a portfolio

Reconcile the bill to attempts, completed work and shared costs30 min · Premium
Serve it from cache, send less, or send it somewhere cheaper30 min · Premium
Test the price ceiling against fixed costs, volume and workload mix30 min · Premium

5. Autonomy over long horizons

An agent that keeps its plan in memory has a plan that ends with the process30 min · Premium
Keep remembered claims current enough and preserve the decisions that matter30 min · Premium
Two hundred agents making the same mistake is one incident, not two hundred30 min · Premium

6. Rescue engineering

You have two days, no context, and everybody has a different theory30 min · Free
The database remembers what the meeting does not30 min · Premium
Build a recovery plan that fits the evidence, dependencies and available time30 min · Premium
Lab: diagnose a failing deployment from exports that disagree with the meeting120 min · Premium

7. Reliability across a portfolio

Twenty deployments, twenty different definitions of working30 min · Premium
Running an incident where every action needs somebody else's hands30 min · Premium
Decide now what the system does on the day the model is unavailable30 min · Premium

8. Extracting a platform from field code

Three customers doing the same thing differently is not yet a product30 min · Premium
Your configuration file has become a programming language nobody can debug30 min · Premium
The customers on the old code did not ask for your platform30 min · Premium

9. The practice

Twenty deployments need one harness and twenty sets of cases30 min · Premium
Turn field observations into a product decision someone can evaluate30 min · Premium
A bottleneck you are proud of is still a bottleneck30 min · Premium
Admit work the practice can finish20 min · Premium
Hire for the work and ramp with evidence25 min · Premium
Plan the portfolio by skill and calendar25 min · Premium
Lab: defend the practice investment90 min · Premium

10. Capstone

One engagement with every constraint at once, and a two-hour defence600 min · Premium

Two hundred agents making the same mistake is one incident, not two hundred

Calculate a fleet alarm caused by changing workload mix, then isolate a real within-segment behavior change. Test scoped tool revocation without pretending it cancels remote jobs, and build rollout and recovery evidence for shared prompts, models and thresholds.

30 MIN · PREMIUM

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The mental model interviewers reward starts one level deeper than the definition: what the concept buys you, what it costs, and the single trade-off that decides when to reach for it.

Work the concrete example end to end, then name the failure mode most candidates miss — that is the signal that you have used this in production, not just read about it.

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PREVIOUSKeep remembered claims current enough and preserve the decisions that matter NEXTYou have two days, no context, and everybody has a different theory30 min · Free

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Read the related research

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2025 · 14 min breakdownWhy Multi-Agent Systems FailBerkeley read 1,642 traces from seven multi-agent frameworks and named 14 ways they fail. Most failures trace back to how the agents are organized and checked, not to the model inside them.Read the summary →
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2026 · 15 min breakdownHarness design for coding agentsHold the agent loop fixed, vary one harness component at a time, and measure. Context management, planning and tool choice each helped some models and hurt or merely cost others.Read the summary →

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