Module 10AI systems and evaluationBoth kinds of role

The agent design round

The agent design round from start to finish: the CTO role-play, agent architecture and tool permissions, stuck agents and budgets, human review and hand-off, evals and rollout, timed prompt tests and voice agents.

7 lessons
1 h 23 min
All in Pro
7 of 7 live
Both kinds of role

By the end of this module you can

Run the round as discovery first, then one page of agent architecture
Scope each tool’s permissions and give the agent memory without leaks
Say what stops a runaway agent, and who finds out
Place a person in the loop and design the hand-off
Evaluate an agent on outcome and trajectory, and roll it out safely

Where this round is reported

Google candidate reportsCandidates reported, in July and August 2026, Google FDE rounds on agentic system design, one of them run as a role-play with the interviewer as a company’s CTO. Source 1FDE Interview Experience at Google (L4)PublisherBlind (teamblind.com)Source typecandidate report on BlindSource 2Google Forward Deployed Engineer Interview ExperiencePublisherBlind (teamblind.com)Source typecandidate report on BlindSource 3Google FDE vs Remain Amazon SDEPublisherBlind (teamblind.com)Source typecandidate report on BlindSource 4Google L5 FDE vs Remain Amazon L5 SDEPublisherBlind (teamblind.com)Source typecandidate report on Blind
Sierra publishedSierra says its engineering onsite includes a build session with AI tools of the candidate’s choice, between a planning session and a review. Source 5The AI-native interview (Vijay Iyengar, Arya Asemanfar, Angie Wang)PublisherSierraSource typecompany blog
Salesforce one candidate reportOne commenter in a Reddit thread about Salesforce’s Agentforce FDE role reported, in December 2025, that their last interview for that position was about AI and Agentforce agents. Source 6Agentforce Forward Deployed Engineer – Final Technical & Behavioral Interview Round? (comment by u/Cold-Information-497)PublisherReddit r/salesforceSource typecandidate report on RedditSource 7Agentforce Forward Deployed Engineer – Final Technical & Behavioral Interview Round? (comment by u/Cold-Information-497)PublisherReddit r/salesforceSource typecandidate report on Reddit
Nugget one candidate reportOne candidate reported, in September 2026, an FDE intern round at Nugget, Zomato’s voice AI team, on agentic workflows, design, voice AI and retrieval. Source 8My Interview Experience: FDE Intern Role at Nugget (Zomato’s Voice AI Team) -EternalPublisherMedium (Divyanshu Rajput)Source typecandidate’s personal write-up

No employer publishes a pass rate or a round weight.

The lessons

7 lessons, 1 h 23 min of reading. 7 of 7 live today.

10.1The agentic design round: a customer’s CTO wants agents14 minProLive

In an agent design round run as a CTO role-play, you can move from discovery to scope, architecture, deployment, security and evaluation, and handle follow-ups on stuck agents, human review and model choice.

What it covers

What candidates report: a role-play round at Google, and the same shape elsewhere
Choosing a design option when you are offered one
Discovery with the CTO: the workflow, the data, the systems the agent will touch
Scoping a first agent a real team would use
The architecture on one page: model, tools, permissions, memory, hand-off
Security, sensitive data and where it runs
How you will know it works, and how it rolls out
Closing the round: what ships, what proves it, what you need

Key takeaways

Answer “where would you start?” by ranking the workflows on what a mistake costs, whether it can be undone and whether the system is ready.
Scope autonomy narrowly and the safety net widely: book in business hours, screen for emergencies around the clock.
Write the design as one page of text: tools and their scopes, identity, knowledge, limits, hand-off, where it runs, what you watch, evidence and rollout.
Close with what ships, the numbers that gate each stage, and what you need from the customer.

Practice

The customer’s chief architect insists on building the agent on their in-house framework, which you think will fail. Respond.

8 more planned.

Do this now

Run the claim-intake case as a design round: five discovery questions, then one page of architecture

10.2Agent architecture: tools, permissions and memory14 minProLive

You can build a minimal agent loop with tools, decide when a fixed workflow is better, design tools a model uses correctly, scope each tool’s permissions, and give an agent memory without leaking data between users.

What it covers

Workflows versus agents
The agent loop
Designing tools the model uses correctly
Tools, permissions and acting on behalf of users
Memory without leaks
The platform view: tool servers and many agents
The answer to the opening question

Key takeaways

Start with a workflow; add autonomy only where it pays.
A tool’s name, description and error messages are part of the prompt; return errors the model can act on.
Give each tool the narrowest permission, and act on behalf of a user only with that user’s rights.
Key memory by the narrowest owner, tenant and lease, never by a unit or phone number that passes between people.

Practice

20 more planned.

Do this now

Find the bugs in the agent’s flow, then write each tool’s permission in one line: whose rights, checked where

10.3Stuck agents, loops and budgets11 minProLive

You can say what stops a runaway agent (step, time and cost budgets, repeated-action detection, circuit breakers), who finds out when it happens, and how the agent fails safely.

What it covers

What one reported screen asks about stuck agents
How agents get stuck: loops, stalls and retries that compound
Budgets: steps, time, tokens and money
Detecting a repeated action
Failing safely: stop, hand off, or ask
Alerts and traces: who finds out, and how fast

Key takeaways

A Blind poster, relaying a Google Senior FDE phone screen that may not be first-hand, listed stuck agents and infinite loops beside guardrails, monitoring and human-in-the-loop.
Give every agent a step budget, a time budget and a cost budget, and decide what happens when each runs out.
Stop a tool call repeated with the same canonical arguments and the same result, and change strategy once instead of retrying forever.
Someone must find out: alert on budget exhaustion and keep a trace that explains the run.

Practice

6 more planned.

Do this now

Build the circuit breaker, then make it trip Kestrel’s agent on stopped runs, across every worker

10.4Human review and hand-off to a person11 minProLive

You can decide which agent actions need a person’s approval, design the hand-off so the person gets the context they need, and keep review from becoming the bottleneck.

What it covers

Where a person belongs in an agent’s loop
Approval before action: what must never run unattended
The hand-off: context, state and the reason for it
Queues, service levels and reviewer load
Learning from what reviewers change

Key takeaways

Require approval for actions that move money past a limit, cannot be undone or reach many people at once, and let the agent do the rest.
Hold the action in a row while it waits, bind the approval to the exact action, let it expire, and re-read state before executing.
A hand-off carries the conversation, what the agent tried, why it stopped and what the tenant was promised, so the person does not start again.
Approval wait climbs steeply as coordinators near full load; size the desk for the busiest hour, and add a person for the peak before you remove a gate.
What reviewers change is evaluation data; feed it back into the golden set, and keep auditing after a limit rises.

Practice

8 more planned.

Do this now

Answer its follow-up: the customer wants approval removed to hit a throughput target. Use your queue numbers

10.5Evals for agents, and rollout11 minProLive

You can evaluate an agent on outcome, tool use and trajectory, with simulated users where needed, and roll it out to one team first with a fallback and a cost you can defend.

What it covers

Outcome versus process
Tool-call correctness
Trajectory review
Simulated users and environments
Cost and step budgets as metrics
Rolling out: one team first, a fallback, and the cost
Back to the question

Key takeaways

Score the outcome first, then the path.
Check that tool arguments are right, not only that tools were called.
Simulated users make multi-turn agents testable.
Roll out to one team first, with a fallback to the old process and a cost per task you can defend.

Practice

Design an evaluation harness that runs before every change to a customer-facing support agent.

8 more planned.

Do this now

Answer the harness question, then add trajectory checks and a pass^k report per task to your design

10.6Timed prompt-engineering tests11 minProLive

In a timed test that grades your prompt against hidden cases, you can write a structured prompt, test it on edge cases you wrote yourself, and iterate inside the time box.

What it covers

What candidates report: Anthropic’s prompt assessment and prompt take-homes
Learning the test format before the clock starts
Structure: role, task, inputs, rules, output format, examples
Edge cases you write yourself
Iterating against failures instead of rewriting
Prompts for voice agents and for model judges

Key takeaways

Posters on Blind report a CodeSignal prompt-engineering assessment for Anthropic roles, and one said working out how the runner read their file left them out of time on the last problem.
Take-home briefs that candidates have posted put a prompt at the center: a voice-agent system prompt in a Bland AI brief, and a story prompt with a model judge in a Hippocratic AI brief.
Learn the test’s format before the clock starts: where the prompt goes, how inputs are filled and how output is graded.
Write your own edge cases with their expected outputs, change one thing at a time, and compare versions by the cases that flipped.

Practice

7 more planned.

Do this now

Write the strict prompt renderer, then five new edge cases for the tenant prompt, each with its expected output first

10.7Voice agents in production: latency, turn-taking and call quality11 minProLive

For a phone or voice agent you can set a latency budget per turn, handle interruptions and objections, and propose how to judge call quality before and after launch.

What it covers

What take-home briefs and postings ask for
One turn: speech in, model, speech out, and where the time goes
Interruptions, silence and hand-off to a person
Objections and scripts: the missed-appointment call
Judging call quality: transcripts, outcomes and sampled listening
What to build first for a customer

Key takeaways

Voice is in the job: postings and launches name voice models and outbound voice agents, and the free voice post lists the take-home briefs.
One candidate reported a Nugget round on voice AI, and one Google candidate was offered an audio and video track in the system architecture round.
Budget and alert on the turn’s end-to-end tail; per-stage numbers tell you where to cut, not whether you are within budget.
Judge call quality on outcomes and sampled listening, not on transcripts alone.

Practice

5 more planned.

Do this now

Solve the p95 question, then answer its follow-up for a voice turn: can you add up each stage’s p95?

Preview a Pro lesson

Lesson 10.114 minProLive

The agentic design round: a customer’s CTO wants agents

What it covers:

What candidates report: a role-play round at Google, and the same shape elsewhere
Choosing a design option when you are offered one
Discovery with the CTO: the workflow, the data, the systems the agent will touch
Scoping a first agent a real team would use
The architecture on one page: model, tools, permissions, memory, hand-off
Security, sensitive data and where it runs
How you will know it works, and how it rolls out
Closing the round: what ships, what proves it, what you need

CTO: I want agents for maintenance, leasing and collections. Where would you start?

The weak answer: “I’d set up an orchestration layer, with a planner and a specialist agent for each department.” It designs a platform before knowing one action an agent may take. The strong answer is a question and a bet:

You: With one of the three, chosen by what a mistake costs. Before I pick: in each department, which requests come most, and which hurts most when it goes wrong?

The free agentic system design post designs one agent for a CTO who has chosen the workflow; this one wants three. This lesson runs the whole round; the next four take its hardest follow-ups in depth: the architecture in code, stuck agents, human review and evaluation.

What candidates report: a role-play round at Google, and the same shape elsewhere

You can say what to expect without over-reading one post. A poster on Blind who collects stories for a site called Chill Interview, so the account may not be first-hand, shared in July 2026 a Google Senior phone screen whose agentic design discussion covered agent orchestration, guardrails, stuck agents and infinite loops, monitoring and human-in-the-loop. Source 1Google Forward Deployed Engineer Interview ExperiencePublisherBlind (teamblind.com)Source typecandidate report on Blind In August 2026, in another user’s Reddit thread about the Google FDE (GenAI) loop, a commenter who named neither the company nor the role reported that the role-related knowledge round meant designing an agent “focused mostly on the enterprise (security, , model routing, multi agent...).” Source 2Google FDE (GenAI) — team match before HC? Curious about others' timelines (comment by u/Firm_Set2163)PublisherReddit r/leetcodeSource typecandidate report on RedditSource 3Google FDE (GenAI) — team match before HC? Curious about others' timelines (comment by u/Firm_Set2163)PublisherReddit r/leetcodeSource typecandidate report on RedditSource 4Google FDE (GenAI) — team match before HC? Curious about others' timelines (comment by u/Firm_Set2163)PublisherReddit r/leetcodeSource typecandidate report on Reddit

One candidate wrote on Reddit, in July 2026, that their recruiter described the Google FDE role-related knowledge round as one business problem designed in a Google Doc, Source 5Need help in preparation for Google Forward Deployed Engineer(FDE) interview (post by u/Wonderful-Ad6418)PublisherReddit r/leetcodeSource typecandidate report on RedditSource 6Need help in preparation for Google Forward Deployed Engineer(FDE) interview (comment by u/Wonderful-Ad6418)PublisherReddit r/leetcodeSource typecandidate report on Reddit so practice writing the design as text, not only drawing it. Outside Google, one candidate’s Medium post, in September 2026, on an FDE intern loop at Nugget, Zomato’s voice AI team, describes an onsite spent mostly on one scenario of automating customer support with AI. Source 7My Interview Experience: FDE Intern Role at Nugget (Zomato’s Voice AI Team) -EternalPublisherMedium (Divyanshu Rajput)Source typecandidate’s personal write-up

None says how the round is scored. Go deep on one scenario, with a line ready for security, retrieval, model choice and multi-agent. Our design rubric gates on a walking skeleton and on observability and evaluation, so the design below has both.

Practice

Cases

Claim intake on a model the insurer already boughtPro
4 planned

Questions

Client scenarios

2 planned

Design prompts

8 planned

Coding drills

9 planned

Take-homes

2 planned

Locked behind Pro

Every lesson in Module 10 is in Pro, the first lesson included, with its preview and practice.

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The evidence behind this module

The plan for this module cites 19 claims: 4 from employers' own pages and postings and 15 candidate reports. How we source what we say.

What Pro adds to this module today

7 of 7 Pro lessons live, and the rest as they ship.
Model answers to 7 questions this module practices Pro

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Questions about Module 10

Is Module 10 free?

No. Every lesson in Module 10 is in Pro, the first lesson included.

How long is Module 10?

7 lessons, 83 minutes of reading in all, plus the practice each lesson ends with.

Is Module 10 written yet?

Yes. All 7 lessons are live.

Which kinds of FDE role is it for?

Both: roles at AI labs and AI product companies, and roles at enterprise software and data companies.

Sources

FDE Interview Experience at Google (L4) Blind (teamblind.com), candidate report on Blind
Google Forward Deployed Engineer Interview Experience Blind (teamblind.com), candidate report on Blind
Google FDE vs Remain Amazon SDE Blind (teamblind.com), candidate report on Blind
Google L5 FDE vs Remain Amazon L5 SDE Blind (teamblind.com), candidate report on Blind
The AI-native interview (Vijay Iyengar, Arya Asemanfar, Angie Wang) Sierra, company blog
Agentforce Forward Deployed Engineer – Final Technical & Behavioral Interview Round? (comment by u/Cold-Information-497) Reddit r/salesforce, candidate report on Reddit
Agentforce Forward Deployed Engineer – Final Technical & Behavioral Interview Round? (comment by u/Cold-Information-497) Reddit r/salesforce, candidate report on Reddit
My Interview Experience: FDE Intern Role at Nugget (Zomato’s Voice AI Team) -Eternal Medium (Divyanshu Rajput), candidate’s personal write-up