Proving Ground
The Forward Deployed Engineer Roadmap For Engineers Who Can Build An Agent But Cannot Yet Defend One
Thirty dated days that take you from a working agent loop to a written case study you can hand a hiring panel, with the failure taxonomy, golden dataset, eval pass rates, and cost per query that FDE interviews actually ask about.
Start Day 1 tonight
Finish the 30 days, walk into a panel, and if you still cannot defend your system, email us for a full refund.
This forward deployed engineer roadmap starts where every tutorial stops. You can already get an agent looping, calling a tool, and returning something that looks right on the happy path, because a coding assistant will write that for you in an afternoon. The demo is not what gets you the offer anymore. What gets you hired at a company placing engineers inside customer environments is the second half nobody teaches: what your agent does when the API times out, how you know it works, what one run costs, and whether you can explain every decision to a skeptical staff engineer without reaching for a slide.
Why FDE Panels Reject Engineers Who Have A Working Demo
Every agent bootcamp, framework quickstart, and YouTube build assumes one thing that turns out to be the entire job: that once the agent produces a correct output on a clean input, you are done. That assumption is exactly where forward deployed engineer interviews live. The panel does not ask you to build an agent. They ask what happens on malformed data, which failure category a wrong-record answer belongs to, how you measured that your fix worked, and what the thing costs to run a thousand times. If your honest answer is "I would add error handling," the loop ends there. The build is now the cheap part, and the evidence and defense that used to be assumed knowledge is the part the offer turns on. That is the part you have never been walked through, because the tutorials hand it off to a future you were supposed to already be.
Introducing Proving Ground
Over thirty dated days you build one agent on a real manual workflow you pick on day six, then push it through the exact progression an FDE is expected to own: recovery, measurement, economics, and defense. Each day is one focused task with a checkpoint, so by day thirty you hold a running system and a written case study that answers the questions a panel and a VP each ask in their own language.
What You Get — $249
The 30-Day Dated Plan — One task per day with a defined checkpoint, structured as four weekly arcs: complete a loop, recover from failure, become measurable, then defend. You always know what today's build is and what "done" means before you start.
Week 1: The Loop That Actually Runs — Days 1 through 7 take you through the agent loop, one API tool plus one web search the agent chooses between, guardrails for input validation and a max-step limit and output filtering, deliberate memory that reaches outside the context window only when state must outlive the run, and an audit trail that logs every prompt, response, tool call, result, error, and timestamp. You end the week running it on one process that used to be manual, tied to a portfolio project.
Week 2: A System That Recovers — Days 8 through 14 convert the demo into something that survives contact with reality: a defined JSON schema instead of free-form text, validation that retries or escalates on a bad structure, a named list of failure modes from missing data to dead APIs to partial completion, state checkpointed every few steps, and a deliberate stop-and-resume from the last checkpoint. You prove it by killing the agent mid-run and watching it pick up where it left off.
Week 3: Measurable And Economically Viable — Days 15 through 21 make the system something you can put numbers on: exponential backoff on every external call at 1, 2, 4, 8, stopping at 16, a six-category failure taxonomy, a golden dataset of 20 real queries hand-labeled across normal, edge, ambiguous, and high-risk, an eval suite scoring correctness and format and tool selection and escalation, cost per query with cheaper models on simple subtasks, and multi-agent decomposition only where it earns its keep.
Week 4: Defend It Like An FDE — Days 22 through 30 are the FDE interview preparation the tutorials skip entirely. You write the pain point, the case for AI over plain software and where autonomy stops, the architecture and why each piece exists, what v1 got wrong and how it changed, your eval pass rates and open risks, and the economics. Then you rehearse the same system two ways: the architecture-and-tradeoffs version for an engineer, and the problem-outcome-evidence-risk version for a VP.
The Case Study Template — The written artifact you hand a panel, pre-structured into the sections FDE interviews probe: workflow audit, architecture, iteration history, evaluation report, deployment controls, and business case. You fill it in as you build, so day thirty ends with it finished rather than started.
The Golden Dataset Kit — A worked example of 20 labeled queries across all four risk tiers, plus the labeling rubric, so your production AI agent portfolio project has an eval set a reviewer will actually respect instead of three cherry-picked prompts.
The Two-Audience Defense Scripts — Question banks and model answers for both interview rooms: the staff engineer drilling into your failure paths and retry logic, and the VP who wants time saved, errors reduced, and cost per query in plain language.
Why $249
A loaded FDE role pays north of $200,000, and the gap between a demo and a defensible system is the single thing standing between you and that offer. A bootcamp charges several thousand dollars to teach the build that a coding assistant now does for free, and stops exactly where the hiring bar begins. Proving Ground is priced as a serious 30-day commitment that a working engineer can expense or absorb without a second thought, and against one interview loop it is a rounding error. If you land the role, the first hour of the first day has paid for it many times over.
Who This Is For
You can get an agent looping and calling a tool, but freeze when a panel asks how it fails.
You have a demo in your portfolio and no eval set, no cost number, and no failure taxonomy to put next to it.
You are a working software engineer targeting an FDE or solutions engineering role at an AI company this quarter.
You are already an FDE or SE who can build fast but wants the vocabulary and evidence to defend systems to senior engineers and executives.
You learn by shipping one real thing daily rather than watching twelve hours of video.
The Day Thirty Guarantee
Follow all thirty days, build the system, write the case study, and take it into a real FDE panel. If you cannot defend your architecture, your failure handling, and your economics when they ask, send us your case study and your notes and we refund every dollar. The deliverable is a defense you can give out loud, and we hold ourselves to that and nothing softer.
In 30 Days, You'll Have:
- A running agent with a loop, two tools, guardrails, deliberate memory, and a full audit trail
- A resumable system with structured outputs, schema validation, checkpointed state, and explicit failure paths
- Retries with backoff, a six-category failure taxonomy, a 20-query golden dataset, and an eval suite with real pass rates
- A cost-per-query number and the model and caching decisions behind it
- A written case study covering workflow audit, architecture, evaluation report, deployment controls, and business case
- A rehearsed engineer-facing defense of your architecture, decisions, failures, and tradeoffs
- A rehearsed VP-facing pitch covering problem, outcome, evidence, and risk in plain language
Frequently Asked Questions
Is this how to become a forward deployed engineer if I have never built an agent before?
It assumes you can write code and read an API doc, and it moves fast from day one. If you have never gotten an agent to loop at all, spend a weekend on a free quickstart first, then start here. Everyone else begins on day one and builds through to a defensible case study.
How is Proving Ground different from an AI agent bootcamp?
A bootcamp spends most of its weeks on the build, which coding assistants have made the cheap part, and ends at a demo. Proving Ground spends week one on the build and the next three on recovery, measurement, and defense, which is the half interviews actually test. You leave with an eval report and a case study rather than a certificate.
How long does each day take, and how much does it cost?
Plan on ninety minutes to three hours per day depending on your pace and the workflow you choose. The roadmap is a one-time $249, includes every template and the golden dataset kit, and you keep all of it. There is no subscription and no cohort schedule to sync to.
I already work as a solutions engineer. Is there anything here for me?
Yes. You will move through week one quickly, and the value for you is weeks three and four: a real eval methodology, a failure taxonomy you can name on the spot, and the two-audience defense scripts that turn a system you built into a system you can present to a staff engineer and a VP without changing the facts, only the language.
What it is: A 30-day dated roadmap that walks you through building one production-grade AI agent and the written case study you defend in an FDE interview.
What you get: The dated day-by-day plan, a case study template, a worked golden dataset kit, and defense scripts for both an engineer panel and a VP.
Price: $249 one-time, everything included, yours to keep.
Catch: It moves fast and assumes you can already write code and get a basic agent looping; the daily builds are real work, not videos to watch.
Guarantee: Finish the 30 days, take it to a panel, and if you cannot defend your system we refund every dollar.
Start Day 1 tonight