Courses / Hard Deployments / Autonomy over long horizons

Lesson 17 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

Keep remembered claims current enough and preserve the decisions that matter

Test timestamped observations at expiry and reject a stale shipment update with a version condition. Separate authoritative run state from fallible summaries, build a bounded context packet with visible omissions, and trace corrections through memory reused across runs.

30 MIN · PREMIUM

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PREVIOUSAn agent that keeps its plan in memory has a plan that ends with the process NEXTTwo hundred agents making the same mistake is one incident, not two hundred30 min · Premium

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Read the related research

See all 33 related papers →

Free summaries. Key takeaways with a free account; deeper analysis with Premium.

2023 · 11 min breakdownLost in the MiddleThe 2023 Stanford paper that showed models use evidence at the start and end of a long prompt far better than in the middle, sometimes worse than no evidence at all.Read the summary →
2025 · 11 min breakdownAgentic Context Engineering (ACE)The 2025 Stanford and SambaNova paper that improves agents by growing a detailed playbook of lessons in their context, with small edits instead of rewrites, and no weight updates.Read the summary →
2023 · 12 min breakdownMemGPTTreat the context window like RAM: a small editable working memory in the prompt, everything else in searchable storage, and the model paging data in and out itself through function calls.Read the summary →

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Harness Engineering for Forward Deployed Engineers →

See where this mechanism sits in the harness around a model: the loop, the instruction map, verification, permissions, credentials, sandboxes, budgets and the customer eval. The 136-page guide carries the 2026 evidence beside each decision, two worked engagements and a tested Python companion. Preview three pages.

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Agentic Database for FDEs →

Place this mechanism in the data architecture behind an agent: authoritative records, retrieval, knowledge graphs, governed tools and safe recovery. The 107-page guide includes three worked cases and an executable companion. Preview three pages.