# CCAR-F Practice: Agentic architecture | Your AI Department > 52 free CCAR-F practice questions on agentic architecture (27% of the exam), with study notes and explanations. Source: https://youraidept.com/network/ccar-f-practice-questions/agentic-architecture CCAR-F domain drill # CCAR-F practice questions: Agentic architecture. 52 original questions on Agentic architecture, which carries 27% of the Claude Certified Architect – Foundations exam per the published guide. Study notes first, then the questions with an explanation for each. When this domain feels solid, take [the timed CCAR-F practice exam](https://youraidept.com/network/ccar-f-practice-exam) and check the domain breakdown. ## What to know about Agentic architecture - The agent loop: the model reasons, calls a tool, receives the result, and decides whether to continue or stop. Know what ends the loop (a final answer, a stop condition, a max-turns limit, a human checkpoint). - Workflows versus agents. Anthropic's own framing: prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer are workflows with fixed control flow; an agent decides its own path. Be able to say which pattern fits a given scenario and why a simpler one is often the right answer. - Orchestrator and subagent designs: what the coordinator keeps, what it delegates, and why subagents get a clean context and a complete brief rather than a pointer to the parent's memory. - Failure handling: retries with backoff, idempotent tool calls, timeouts, and what the system does when a tool returns an error instead of a result. - Guardrails and human-in-the-loop: where approval gates belong (spend, deletion, external sends), and how hooks or permission checks enforce them outside the model's judgment. - Cost and latency trade-offs: model choice per step, parallel tool calls, caching, and when a deterministic function beats a model call. ## Agentic architecture questions All domains (52) Agentic architecture (52) Agentic architecture A support desk needs incoming tickets sorted into five fixed categories and routed to the matching queue. Which design fits best? - An evaluator-optimizer loop that proposes a category and revises it until a second judge call agrees - A single autonomous agent with the queue tools attached, deciding per ticket where it should go - A routing workflow with one classification call, then a fixed handoff to the queue for that category - An orchestrator that spawns five subagents and lets each one claim the tickets it judges to be in scope Check answer ### Show answer Answer: A routing workflow with one classification call, then a fixed handoff to the queue for that category. The control flow is known in advance and the output space is fixed, so a routing workflow (classify, then hand off) is cheaper, faster, and easier to test. The evaluator-optimizer loop adds iterations to a decision that has nothing to refine, and an orchestrator with five subagents spends five calls to make one choice. Agentic architecture A task requires editing an unknown number of files across a repository, and the set of files cannot be predicted until the work is under way. Which pattern applies? - Parallelization with a worker per file type, each scanning its share of the tree for changes to make - Prompt chaining with one step per directory, so every part of the repository gets its own fixed pass - Orchestrator-workers, with a coordinator that discovers the subtasks during the run and delegates each one - A single call with the full repository loaded into context, asking the model to return every edit in one response Check answer ### Show answer Answer: Orchestrator-workers, with a coordinator that discovers the subtasks during the run and delegates each one. Orchestrator-workers is the pattern for work whose subtasks are only discoverable during execution; the coordinator plans, delegates, and synthesizes. Chaining by directory and fanning out by file type both fix the decomposition before the work starts, which is exactly what this task does not allow. Agentic architecture A translation pipeline produces drafts that a reviewer can grade against clear criteria, and quality improves when feedback is applied. Which pattern matches? - Routing by language pair, so each pair reaches a specialist prompt tuned with curated examples for that pair - Parallelization across sentences, with each worker translating a slice and a final merge step - Evaluator-optimizer, where one call drafts, a second grades against the criteria, and the loop repeats - A single agent with a translation tool that it calls once per document and returns the result Check answer ### Show answer Answer: Evaluator-optimizer, where one call drafts, a second grades against the criteria, and the loop repeats. Evaluator-optimizer fits when there are explicit evaluation criteria and iterative refinement measurably helps. Routing by language pair may improve the first draft but has no feedback step, so the stated gain from applying review is left on the table. Agentic architecture An agent can issue refunds. The business wants any refund above $500 approved by a person. Where should that rule live? - In a permission check or hook that intercepts the refund tool call and blocks it until a person approves - In a weekly review of the refund log, so any refund over the limit is caught and reversed if it turns out to be wrong - In the system prompt, as a rule telling the model to pause and ask before any refund above the limit - In the refund tool's description, so the model reads the threshold every time it considers the tool Check answer ### Show answer Answer: In a permission check or hook that intercepts the refund tool call and blocks it until a person approves. Hard limits belong outside the model's judgment. A hook or permission gate enforces the rule even when the prompt is ignored or the model is confused. The system prompt and the tool description are both advisory: they improve the odds but cannot guarantee the check runs. Agentic architecture A tool that creates invoices sometimes times out, and the agent retries. Customers are receiving duplicate invoices. Best fix? - Make the tool idempotent with a client-supplied request id, so a repeated call returns the same invoice - Raise the tool's timeout so that slow invoice runs finish before the agent loop gives up on the call and retries - Add a system prompt rule that the model should retry only after checking the invoice list first - Move invoice creation out of the tool and into a post-run batch job that dedupes by customer Check answer ### Show answer Answer: Make the tool idempotent with a client-supplied request id, so a repeated call returns the same invoice. Retries are normal in agent loops. Tools with side effects need idempotency so a repeated call is safe. Raising the timeout only narrows the window in which the duplicate can happen, and a check-before-retry rule depends on the model following it under every failure mode. Agentic architecture During a long multi-agent run one tool starts returning errors on every call, and the coordinator keeps re-issuing the same request. What is missing? - A stronger model for the coordinator role so it notices the pattern and changes its approach - A larger context window for the coordinator so the earlier error results stay visible to it - Bounded retries with backoff and a circuit breaker that stops calling the tool after repeated failures - A system prompt instruction telling the coordinator to switch to a different tool as soon as it sees an error result Check answer ### Show answer Answer: Bounded retries with backoff and a circuit breaker that stops calling the tool after repeated failures. Failure handling is part of the architecture: cap retries, back off, and stop or escalate when a dependency is down. A prompt instruction may help a well-behaved model but is not a control; the loop itself must enforce the limit. Agentic architecture A document pipeline runs the same three steps on every input: extract entities, normalize them, then write a summary. Each step's output feeds the next. Which pattern is this? - Orchestrator-workers, with a planner assigning steps - Evaluator-optimizer over the summary step - Parallelization, with the three steps run at once - Prompt chaining, with each output feeding the next Check answer ### Show answer Answer: Prompt chaining, with each output feeding the next. Fixed sequential steps where each output feeds the next is prompt chaining. Parallelization does not fit because normalize depends on extract; an orchestrator adds planning cost and nondeterminism to a path that never changes. Agentic architecture Three independent checks (toxicity, PII, and policy) must run on every response before it is shown. Latency matters. Which pattern? - Have one classifier call decide which checks are needed - Fold all three checks into a single call with one prompt - Chain the checks in sequence so each one sees the previous verdict - Run the three checks concurrently and aggregate the verdicts Check answer ### Show answer Answer: Run the three checks concurrently and aggregate the verdicts. Independent work with no dependencies is the parallelization pattern; running the checks concurrently cuts latency. Folding them into one call is tempting but mixes three jobs in one prompt and makes each verdict harder to test on its own. Agentic architecture When does Anthropic's guidance say to reach for an agent instead of a workflow? - When the task is large enough that a single prompt cannot hold all of the input - When the number of steps cannot be predicted and the model must choose its path from results - When latency is not a concern and the budget comfortably allows several model calls for each request - When the steps are known in advance but each step needs a different model Check answer ### Show answer Answer: When the number of steps cannot be predicted and the model must choose its path from results. Agents earn their cost when the path is open-ended and the model must decide the next step from what it finds. Known steps with different models is a workflow with per-step model choice; a large input calls for retrieval or chunking, not autonomy. Agentic architecture An agent for booking travel has succeeded in demos but sometimes loops forever in production. The most important missing control is: - A maximum turn count and a stop condition the loop checks on every iteration - A larger context window so earlier attempts remain visible and are not repeated - A wider tool set so the agent has an alternative route when one provider fails - A more detailed persona and goal statement so the agent stays focused on the booking Check answer ### Show answer Answer: A maximum turn count and a stop condition the loop checks on every iteration. Every agent loop needs an explicit termination condition and a hard cap on turns, or a confused model runs up the bill. A larger window makes the loop remember more but does not end it; more tools give it more ways to keep going. Agentic architecture Which is the strongest reason to start a new client project with workflows rather than agents? - Workflows can be built without prompt engineering, so the client's own team can maintain them - Workflows are what stakeholders expect and are easier to present in a demo - Workflows are predictable, testable, and cheaper, building the trust needed before autonomy - Agents require the Agent SDK, which most client environments cannot run Check answer ### Show answer Answer: Workflows are predictable, testable, and cheaper, building the trust needed before autonomy. Predictability and testability come first. Autonomy is added where the evidence says it pays. Workflows still contain prompts, so maintainability without prompt work is not the argument, and the API supports agents without any special runtime. Agentic architecture An orchestrator delegates research to three subagents and then writes a report. One subagent returns nothing useful. What should the design do? - Drop that topic from the report and note in the log that the subagent returned no content - Hand the empty topic back to the user with a question about whether the report should continue - Detect the gap, retry or re-scope the subtask, and proceed with what it has when retries run out - Fail the whole run and surface the empty result, since a partial report could mislead whoever reads it Check answer ### Show answer Answer: Detect the gap, retry or re-scope the subtask, and proceed with what it has when retries run out. Robust orchestration treats subagent failure as a normal event with a recovery path and a bounded retry budget. Failing the whole run throws away two good results for one bad one; dropping the topic silently hides the gap from the reader. Agentic architecture A coding agent must never run a destructive git command. Which enforcement is reliable? - A CLAUDE.md section describing which git operations are off limits in this repo - A PreToolUse hook or permission rule that blocks matching commands before they execute - A lower temperature setting so the agent sticks to the conservative commands it has already seen - A system prompt rule listing every destructive git command the agent must not run Check answer ### Show answer Answer: A PreToolUse hook or permission rule that blocks matching commands before they execute. Deterministic gates outside the model are the reliable layer. Prompts and CLAUDE.md are advisory: they lower the odds of a bad call but cannot block one, and temperature changes sampling, not policy. Agentic architecture You are asked to justify why an agent design uses a single coordinator instead of a swarm of peer agents that message each other freely. The best justification: - Peer messaging is slower in practice because every message needs its own model call - A coordinator keeps control flow observable and debuggable, and bounds each subagent's context - Peer agents cannot share tool results, so a coordinator is needed to relay them - The API's multi-agent support is built around a single coordinator, so peer swarms need extra scaffolding Check answer ### Show answer Answer: A coordinator keeps control flow observable and debuggable, and bounds each subagent's context. Hub-and-spoke designs are easier to trace, test, and constrain than free-form peer messaging. The speed argument is not reliable, since a coordinator also spends calls; the API does not mandate either topology. Agentic architecture A customer wants an agent that autonomously sends emails on their behalf. What belongs in the first release? - Autonomous sending for all emails except the ones the model itself flags as sensitive - Draft-then-approve: the agent prepares emails and a person sends, with autonomy earned on measured accuracy - Autonomous sending for internal recipients only, expanding to external once a week has passed - Full autonomy from day one, with a daily audit of what was sent so problems are caught early Check answer ### Show answer Answer: Draft-then-approve: the agent prepares emails and a person sends, with autonomy earned on measured accuracy. Outbound actions with reputational risk start behind a human gate; autonomy expands as evidence accumulates. Letting the model flag its own sensitive cases makes the model the gate it is supposed to be behind; a daily audit catches problems after the email has gone out. Agentic architecture Which metric best tells you an agent is worth its cost in production? - Average tokens consumed per completed task, trended week over week - Number of tasks handled per day compared with what the human team handled before - Task completion rate at acceptable quality, measured per dollar and per minute - Share of runs that finish without any tool errors or retries Check answer ### Show answer Answer: Task completion rate at acceptable quality, measured per dollar and per minute. Outcome per cost is the measure. Tokens per task is an input, not value; runs without tool errors can still produce wrong answers; tasks per day ignores quality and cost. Agentic architecture An evaluator-optimizer loop keeps running for many iterations without converging. What is the likely design flaw? - The evaluator's criteria are vague, so 'good enough' is never reached; it needs pass criteria and a round cap - The loop has no memory of earlier drafts, so it keeps repeating the same corrections - The generator runs at too high a temperature, so each draft moves in a new direction - The evaluator and generator share a prompt, so the judge is biased toward its own output Check answer ### Show answer Answer: The evaluator's criteria are vague, so 'good enough' is never reached; it needs pass criteria and a round cap. Refinement loops need crisp acceptance criteria and a maximum number of rounds. Temperature and draft memory can add noise, but a loop with a clear pass condition and a cap terminates regardless; without them, no tuning of the generator helps. Agentic architecture A routing workflow classifies requests into billing, technical, and sales. Ten percent of requests fit none. What should the design include? - A fourth category learned from the unclassified requests after launch - Retry classification with a second model until every request lands in one of the three - A fallback route for 'other' that goes to a general handler or to a person - A confidence threshold that forces low-confidence requests into the nearest bucket Check answer ### Show answer Answer: A fallback route for 'other' that goes to a general handler or to a person. Routers need an explicit fallback path; forcing fits produces confident wrong handling. Retrying with another model still forces a fit, and a nearest-bucket threshold is the same forced fit with a number attached. Agentic architecture Which statement about tools in agent loops is correct? - Tools must be called in the order they are listed, so ordering in the tools array matters - Tool results are data the model reasons over; instructions come from the system prompt and user - Tool results can only carry text, so binary or structured data must be described in the prompt - Tool results should be passed back as a user turn so that the model treats them as fresh instructions to follow Check answer ### Show answer Answer: Tool results are data the model reasons over; instructions come from the system prompt and user. Treating tool output as data rather than instructions is the basis of injection resistance and clean reasoning. Tool results do travel in a user turn as tool_result blocks, but that is a transport detail; it does not make them instructions. Agentic architecture A team wants to run twenty subagents in parallel for a research task. What should they watch first? - That each subagent has a complete, self-contained brief and a bounded budget - That every subagent uses the same tool set so the results have a common shape - That the coordinator holds no context of its own and only merges the results - That all subagents share one context window so they do not duplicate work Check answer ### Show answer Answer: That each subagent has a complete, self-contained brief and a bounded budget. Parallel fan-out multiplies whatever you give it. Clear briefs and budgets per worker keep it useful. A shared window is not how subagents work and would defeat the isolation; identical tool sets do not make briefs clearer. Agentic architecture An agent must decide between continuing a task and asking the user a question. Good design says it should ask when: - Any tool call in the current step has returned an error - It has consumed half of its token budget for the task at hand - It has finished one subtask and the next one on the list belongs to a different owner - The next action is irreversible, ambiguous, or outside its granted scope Check answer ### Show answer Answer: The next action is irreversible, ambiguous, or outside its granted scope. Human checkpoints belong at irreversible or ambiguous decisions, not at arbitrary points. A tool error is usually something the loop should handle with a retry or an alternate path; a token milestone says nothing about risk. Agentic architecture A workflow uses one large model call to do classification, extraction, and a summary at once, and quality is uneven. What is the usual improvement? - Decompose into chained steps so each call has one job and can be checked - Drop the summary from the call and generate it separately with a template - Switch to a larger model so it can handle the three tasks in one pass - Enable extended thinking so the model plans all three outputs before writing Check answer ### Show answer Answer: Decompose into chained steps so each call has one job and can be checked. Splitting a multi-task prompt into single-purpose steps improves accuracy and makes each step testable. Extended thinking helps hard reasoning but does not fix a prompt that mixes three jobs; dropping one task is only a partial decomposition. Agentic architecture What is the practical reason to log every tool call, its inputs, and its result in an agent system? - Cost control, since tool call logs are the only way to reconstruct token usage per task - Prompt caching, since logged calls can be replayed as a cached prefix on the next run - Debugging, evaluation, and audit: you cannot improve or explain behavior you did not record - Compliance reporting, since most regulated industries require a record of automated actions Check answer ### Show answer Answer: Debugging, evaluation, and audit: you cannot improve or explain behavior you did not record. Observability is how agent behavior is understood, evaluated, and defended. Compliance is one use, not the practical reason; usage is reported per request by the API, and logs have nothing to do with cache prefixes. Agentic architecture A client says 'make it fully autonomous' for an agent that changes production database records. Your recommendation as the architect: - Full autonomy as requested, with a nightly database backup so any bad change can be restored - Full autonomy with logging turned off in the hot path so approval latency does not slow the agent - Read-only access for the agent, with all writes handled by a separate scheduled job the client owns - Autonomy for reads and reversible changes, approval for irreversible ones, with an audit trail, revisited as data comes in Check answer ### Show answer Answer: Autonomy for reads and reversible changes, approval for irreversible ones, with an audit trail, revisited as data comes in. Blast radius sets the gate. Irreversible actions get approval until evidence justifies more. A nightly backup limits damage after the fact but does not prevent a customer-visible change; read-only for everything gives up the value the client asked for. Agentic architecture A due-diligence agent must read 300 contracts and produce one risk table. Which structure keeps quality consistent? - Load all 300 contracts into one long context and ask for a single risk table in one pass - Fan out one worker per contract with the same extraction brief and schema, then merge the structured results - Route each contract by type to a specialist prompt and let each specialist write its own section of the table - Chain the contracts sequentially so each worker sees the previous worker's risk rows before extracting its own Check answer ### Show answer Answer: Fan out one worker per contract with the same extraction brief and schema, then merge the structured results. Uniform per-item briefs and structured returns make fan-out work reproducible and mergeable. Chaining contracts sequentially lets early errors contaminate later extractions and adds no consistency; routing by type produces sections written to different standards. Agentic architecture What is the main cost of adding a 'reflection' step where the agent critiques its own output before returning it? - Added tokens and latency, so it pays off where errors are expensive and the criteria are checkable - It blocks tool use during the critique turn, so agents that rely on tools cannot use reflection safely - It only works with a larger model as the critic, which doubles the cost of every task in the pipeline - The critique tends to anchor on the draft, so it should replace the generator rather than follow it Check answer ### Show answer Answer: Added tokens and latency, so it pays off where errors are expensive and the criteria are checkable. Self-critique costs tokens and time, so it belongs on high-stakes, checkable work and wastes budget elsewhere. A critic does not need a larger model, does not block tools, and is meant to follow the generator, not replace it. Agentic architecture An agent is allowed to call a payments API. Which control is non-negotiable? - An evaluator model that reviews each proposed payment call and rejects any that look unusual - An enforced gate outside the model that requires approval or applies limits before the call executes - A system prompt instruction to confirm the amount with the user before every payment call - A detailed tool description that lists the payment limits so the model knows when to refuse a transfer Check answer ### Show answer Answer: An enforced gate outside the model that requires approval or applies limits before the call executes. Money moves only through gates enforced in code, not on the model's judgment. Descriptions, prompt instructions, and an evaluator model all rely on a model following guidance, which is not a hard control. Agentic architecture Which sign suggests a workflow should be split into two chained model calls? - The task takes several seconds per run, and latency matters more than accuracy for this use case - The prompt calls a single tool several times per run, and each call returns a large result - One prompt both extracts facts and writes persuasive prose, and one of those two keeps degrading - Outputs are short and consistent, but the prompt is longer than the team would like to maintain Check answer ### Show answer Answer: One prompt both extracts facts and writes persuasive prose, and one of those two keeps degrading. Competing objectives in one call degrade both; chaining separates extraction from writing. Large tool results are a context problem, not a chaining signal, and chaining adds latency rather than removing it. Agentic architecture A team runs an agent that reads email and drafts replies. A client asks why it never sends automatically. Best answer? - Autonomous sending would break prompt caching because each sent message changes the conversation prefix - The Messages API has no outbound email capability of its own, so a person has to press send in the mail client each time - Sending is irreversible and public, so it stays gated by a person until measured accuracy earns more autonomy - The agent runs in a workflow rather than an agent loop, and workflows cannot trigger external actions Check answer ### Show answer Answer: Sending is irreversible and public, so it stays gated by a person until measured accuracy earns more autonomy. Autonomy follows evidence; irreversible actions start gated. Sending is a tool the team could add at any time, so the limit is a design choice, not an API or caching constraint. Agentic architecture What distinguishes routing from an orchestrator in the workflow patterns? - Routing requires a separate model per path, while an orchestrator uses one model for every subtask - Routing sends an input down one of several fixed paths; an orchestrator plans dynamic subtasks and synthesizes results - Routing runs several fixed paths in parallel and merges their outputs; an orchestrator runs the same paths one after another and keeps the best - Routing is a form of parallelization; an orchestrator is a form of evaluator-optimizer loop Check answer ### Show answer Answer: Routing sends an input down one of several fixed paths; an orchestrator plans dynamic subtasks and synthesizes results. Routing classifies an input and picks a fixed path; orchestration plans subtasks that were not known in advance and merges the results. Running paths in parallel and merging describes parallelization, not routing. Agentic architecture An agent produced a correct answer but took 40 tool calls where 5 would do. Which lever addresses this? - Sharper tool descriptions, an explicit plan step, and a per-task call budget with feedback as it nears the limit - Cut the tool list down to the two or three tools the task used most, so there is less to choose from and fewer wrong turns - Switch to parallel tool calls so the 40 calls complete in the time five sequential ones would take - Lower the context window by trimming the system prompt so the model has less to reason over each turn Check answer ### Show answer Answer: Sharper tool descriptions, an explicit plan step, and a per-task call budget with feedback as it nears the limit. Tool clarity, planning, and budgets reduce thrashing. Removing tools may break other tasks, and parallel calls hide the waste instead of removing it. Agentic architecture Why should a subagent's brief include the success criteria and the expected return format? - So the coordinator can reuse the subagent's full transcript as context for later steps instead of a lossy summary - So the subagent knows when to stop and the coordinator can merge results without re-reading transcripts - So the subagent can skip tool calls and answer from the brief alone, saving tokens - Because the Agent SDK rejects a subagent definition that has no output schema attached Check answer ### Show answer Answer: So the subagent knows when to stop and the coordinator can merge results without re-reading transcripts. Clear criteria and formats make delegation composable: the worker stops at the right point and the coordinator merges structured returns. Reusing full transcripts is the thing a good brief avoids. Agentic architecture A stakeholder asks for 'an AI that runs the whole department'. As the architect, the first design move is: - Build one general agent with every department tool attached and let it learn the workflows from use - Start with an orchestrator-workers system so each function of the department gets its own dedicated worker agent - Map the workflows, classify steps by predictability and blast radius, and start with the predictable ones - Pick the most capable model available so the agent can handle whatever the department throws at it Check answer ### Show answer Answer: Map the workflows, classify steps by predictability and blast radius, and start with the predictable ones. Decompose, classify, and start where reliability is easy to prove. Jumping to an orchestrator or a single general agent commits to an architecture before the work has been understood. Agentic architecture Which is the correct role of an evaluator in an evaluator-optimizer loop? - Produce the first draft, then revise it against the same criteria in a single turn - Decide which tools the generator may call on its next attempt at the task - Summarize the generator's transcript so the loop fits in the context window - Judge the draft against explicit criteria and return specific, actionable feedback Check answer ### Show answer Answer: Judge the draft against explicit criteria and return specific, actionable feedback. Evaluators judge against criteria and feed back; generators produce. Drafting and revising in one turn is self-critique, not the two-role loop. Agentic architecture An agent needs to process items that arrive continuously over a day. What design fits? - A single batch prompt at the end of the day that processes all items together in one call - One long-running session that keeps every item in context so it can spot patterns across the day - A queue-driven loop that handles each item in a fresh bounded context and persists state between items - An orchestrator that spawns a subagent per item and keeps every subagent transcript for the coordinator Check answer ### Show answer Answer: A queue-driven loop that handles each item in a fresh bounded context and persists state between items. Per-item bounded contexts with persisted state scale and stay reliable. A day-long session overflows, an end-of-day batch loses timeliness, and keeping every transcript in the coordinator recreates the overflow. Agentic architecture What is the practical reason to make agent steps observable with structured logs rather than free-text notes? - Structured logs can be queried, rolled into metrics, and replayed for evaluation - The model produces more consistent reasoning when it is asked to log in a structured format - Free-text notes consume more tokens per step, so structured logs keep the run inside the context budget - Structured logs are required for prompt caching to work across steps of the run Check answer ### Show answer Answer: Structured logs can be queried, rolled into metrics, and replayed for evaluation. Structure makes behavior measurable and replayable. Logs live outside the context window, so token savings and caching are not the reason. Agentic architecture A coordinator receives conflicting answers from two subagents. Sound handling? - Take the answer from whichever subagent used more tool calls, since it did more research - Merge both answers into one combined response and let the downstream consumer decide which parts to trust - Detect the conflict, resolve it with a defined rule or a verification step, and record the decision - Rerun both subagents with the same brief and accept the answer that appears more often Check answer ### Show answer Answer: Detect the conflict, resolve it with a defined rule or a verification step, and record the decision. Conflicts need explicit resolution and a record, not a silent choice. Tool-call count is not evidence of correctness, and majority reruns can repeat the same error. Agentic architecture Which situation calls for human-in-the-loop by design rather than by exception? - Any step where the model reports low confidence in its own answer partway through the run - Approving legal commitments, spending above a threshold, or changes to people's records - Steps where two workers in a parallel fan-out return different formatting - Every tool call that reads from a production database rather than a staging copy Check answer ### Show answer Answer: Approving legal commitments, spending above a threshold, or changes to people's records. High-consequence categories get a standing human step. Low model confidence triggers a review by exception, and reads or formatting mismatches are handled in code. Agentic architecture What is the risk of letting an agent modify its own instructions during a run? - The run costs more because each instruction rewrite invalidates the cached prompt prefix - There is little risk if every instruction change is logged, since the log lets you replay the run afterward - Drift and loss of control; standing instructions should be fixed, versioned, and changed by people - The agent becomes slower to complete tasks because it spends turns editing its own prompt Check answer ### Show answer Answer: Drift and loss of control; standing instructions should be fixed, versioned, and changed by people. Self-modifying instructions undermine predictability and audit. Cache and speed costs are minor by comparison, and logging a drift does not prevent it. Agentic architecture A workflow's third step frequently fails on malformed input from step two. Best fix? - Validate step two's output against a schema before step three runs, with a repair path on failure - Merge steps two and three into one prompt so there is no boundary for malformed data to cross - Wrap step three in a retry loop with backoff so transient formatting problems resolve themselves - Ask step three to tolerate malformed input by instructing it to do its best with whatever arrives Check answer ### Show answer Answer: Validate step two's output against a schema before step three runs, with a repair path on failure. Validate at boundaries; repair or route on failure. Retries do not fix deterministic malformation, and merging steps hides the fault instead of catching it. Agentic architecture A nightly job classifies 50,000 support tickets into one of eight categories. Which architecture is right? - One agent with the full ticket export in context so it can spot category patterns - An autonomous agent per ticket with tools to look up the customer's history before deciding - An orchestrator that spawns one classifier subagent per category and lets them vote on each ticket - A batch workflow with a fixed prompt and schema-constrained output, plus a sampled human audit Check answer ### Show answer Answer: A batch workflow with a fixed prompt and schema-constrained output, plus a sampled human audit. Fixed labels, huge volume, no branching: a batch workflow with constrained output and an audit sample. An agent per ticket adds cost and variance to a task with no open-ended path. Agentic architecture Which question best decides whether a task needs an agent at all? - Can the steps be written down in advance, or does the path depend on what the model finds along the way? - Is the budget large enough to absorb the extra tokens that an agent loop consumes? - Does the task involve tools, since tool use is what distinguishes an agent from a prompt? - Is the model capable enough to complete the task end to end without a human checking each intermediate step? Check answer ### Show answer Answer: Can the steps be written down in advance, or does the path depend on what the model finds along the way? Agents are for open-ended paths; predictable steps are workflows or code. Workflows can use tools too, so tool use alone does not decide it. Agentic architecture An agent's outputs are correct but users complain it 'never explains why'. What is the low-cost design fix? - Enable extended thinking and return the thinking blocks to users as the explanation - Ask for a brief rationale alongside the structured result, kept separate from the machine-readable fields - Add a second model call that reads the result and writes an explanation for the user - Drop the structured output and return prose so the reasoning and the answer arrive together in one readable block Check answer ### Show answer Answer: Ask for a brief rationale alongside the structured result, kept separate from the machine-readable fields. A separate rationale field gives users the why without breaking parsing. Thinking blocks are not written for end users, and a second call doubles cost for something the first call can produce. Agentic architecture Two subagents produce partial reports and the coordinator must merge them into one document. What protects merge quality? - A fixed output schema for the partials so merging is deterministic, then one editing pass for coherence - Run the two subagents sequentially so the second one extends the first one's document - Let the coordinator read both partials in full and improvise a merged document - Have each subagent read the other's draft before finishing so the two partials already agree on structure and content Check answer ### Show answer Answer: A fixed output schema for the partials so merging is deterministic, then one editing pass for coherence. Structured partials make the merge reliable; a final pass restores flow. Cross-reading drafts couples the workers and loses the parallelism that made the split worthwhile. Agentic architecture Which is the right response when an agent's evaluator step disagrees with a human reviewer 30% of the time? - Average the evaluator's score with the human's so neither judgment dominates the outcome - Trust the evaluator, since consistent model judgments beat a single reviewer's opinion - Calibrate the evaluator against the human labels, refine its criteria, and keep the human until agreement is high - Replace the evaluator with a second human reviewer, since a 30% disagreement rate shows model grading does not work here Check answer ### Show answer Answer: Calibrate the evaluator against the human labels, refine its criteria, and keep the human until agreement is high. Evaluators earn autonomy through measured agreement. Averaging hides the disagreement instead of resolving it, and abandoning the evaluator throws away a fixable component. Agentic architecture A client wants the agent to run overnight unattended. Which capability must exist first? - Extended thinking on every turn so the agent reasons carefully without supervision - A hard stop on cost and turns plus alerting, so an unattended run cannot spiral - A dashboard showing live progress so the team can check on it in the morning - A larger model, so the run makes fewer mistakes when nobody is there to correct it Check answer ### Show answer Answer: A hard stop on cost and turns plus alerting, so an unattended run cannot spiral. Unattended means bounded and observable. A dashboard shows a spiral but does not stop it. Agentic architecture What does 'fail closed' mean for an agent that hits an unexpected error? - Stop the action, record the state, and hand off to a person or a defined fallback - Retry the failing step with a different tool until one of them succeeds - Continue with the most likely interpretation and flag the guess in the final report - Restart the whole task from the beginning with a fresh context and the same inputs Check answer ### Show answer Answer: Stop the action, record the state, and hand off to a person or a defined fallback. Fail closed protects against acting on bad state. Continuing on a guess is failing open, and blind retries can repeat a harmful action. Agentic architecture An orchestrator-workers design is producing repeated work across workers. Likely cause? - Too few workers, so each one is covering more of the task than it can hold in context - Overlapping subtask briefs; the coordinator should partition the task so each worker's scope is disjoint - Missing evaluator step, so duplicate outputs are never caught before the merge - The workers share one context window, so each one sees the others' progress and repeats it in its own output Check answer ### Show answer Answer: Overlapping subtask briefs; the coordinator should partition the task so each worker's scope is disjoint. Disjoint scopes prevent duplicate effort. Workers have separate contexts, and an evaluator would catch duplicates after the wasted work rather than prevent them. Agentic architecture Which metric would you add to detect that an agent is 'succeeding' by giving up early? - Task completion checked against explicit acceptance criteria, not the model reporting done - Tool calls per task, since an agent that stops early makes fewer calls than expected - Latency per task, since early exits finish faster than runs that do the whole job - Average tokens per task, since a run that gives up early uses noticeably fewer tokens than a full run Check answer ### Show answer Answer: Task completion checked against explicit acceptance criteria, not the model reporting done. Verify completion against criteria, not self-report. Token, call, and latency counts are proxies that an efficient run and an early exit can share. Agentic architecture A workflow needs a human to approve step three only when the amount exceeds a threshold. How do you model it? - Instruct the model to decide when the amount seems high enough to ask a person - Require approval on every run of step three, since a threshold check adds complexity - A conditional gate in the workflow that routes to a human queue when the condition holds - Have an evaluator model review step three's output and escalate whenever the amount looks risky Check answer ### Show answer Answer: A conditional gate in the workflow that routes to a human queue when the condition holds. Conditional gates are code, not model judgment. The threshold is known, so an evaluator's opinion adds variance where a comparison would do. Agentic architecture Which practice keeps agent behavior reproducible enough to debug? - Logging the exact prompts, tool inputs and outputs, and model settings per step - Using the largest model, since stronger models behave more predictably - Turning off prompt caching so each step is computed fresh from the raw prompt - Setting temperature to zero, which makes each run identical for the same input and settings Check answer ### Show answer Answer: Logging the exact prompts, tool inputs and outputs, and model settings per step. Reproduction needs the full record. Temperature zero does not guarantee identical outputs and does not tell you what the tools returned. Agentic architecture A stakeholder asks why the agent needs a 'planning' step that costs extra tokens. Best answer? - It makes the model look more deliberate, which builds stakeholder confidence in the output even when the plan is not followed - The Agent SDK requires a planning turn before any tool call can be executed in a session - Plans are cached as part of the prompt prefix, so every later turn in the same run costs less per token than the first - On multi-step tasks a plan cuts wasted tool calls and lets people review intent before actions, usually saving more than it costs Check answer ### Show answer Answer: On multi-step tasks a plan cuts wasted tool calls and lets people review intent before actions, usually saving more than it costs. Plans cut thrash and enable review of intent before side effects. Nothing in the SDK requires a plan, and a plan in the conversation is not a cached prefix. ## Next - [Timed CCAR-F practice exam](https://youraidept.com/network/ccar-f-practice-exam): 60 questions in 120 minutes, scored against the 720 pass mark. - [All CCAR-F practice questions](https://youraidept.com/network/ccar-f-practice-questions) with a domain filter. - [The CCAR-F exam page](https://youraidept.com/network/ccar-f): format, weights, registration, study path. ### Other CCAR-F domains - [Claude Code workflows (40 questions)](https://youraidept.com/network/ccar-f-practice-questions/claude-code-workflows) - [Prompt engineering (40 questions)](https://youraidept.com/network/ccar-f-practice-questions/prompt-engineering) - [Tool design and MCP (35 questions)](https://youraidept.com/network/ccar-f-practice-questions/tool-design-and-mcp) - [Context management (33 questions)](https://youraidept.com/network/ccar-f-practice-questions/context-management) ## FAQ ### How much of the CCAR-F exam is agentic architecture? + 27% of the marks, per the published CCAR-F exam guide. The full domain list with weights is on the CCAR-F exam page. ### Are these real CCAR-F exam questions? + No. They are 52 original scenario questions written from the blueprint for this domain. Reproducing exam content would violate the candidate agreement. ### How should I study agentic architecture for CCAR-F? + Read the domain notes on this page, answer the questions with explanations hidden, then take the timed CCAR-F practice exam and check the per-domain breakdown for this domain. ## Who wrote this ### [Matei Olaru](https://youraidept.com/about/matei-olaru) CEO, Your AI Department Former public-company CEO, corporate attorney, and World Bank consultant. Runs the YAID Network and the partner relationship with Anthropic. ### [Michael Greenberg](https://youraidept.com/about/michael-greenberg) AI Product Lead, Your AI Department AI at Google, Meta, and Samsung. Six patents. Leads member support and the certification prep guidance. Your AI Department is a registered Claude Partner Network company. Our members sit these exams under our partner standing. Facts on this page were last checked on October 15, 2026. ## Ready to sit CCAR-F for real? Registration needs a Claude Partner Network organization. [YAID Network](https://youraidept.com/network) members register under our partner standing. $149 per quarter, exam fee separate. [Apply to join YAID ↗](https://youraidept.com/network/apply?ref=drill)[How registration works →](https://youraidept.com/network/register-for-claude-certification)