5-Phase Framework
A repeatable opening for any agentic system-design round: restate the problem, ask until the next answer would not change the design, derive one number, walk one request down the six-layer spine, and harden one concern at a time.
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After this section you can
- Run the five phases in order inside a 45-minute round
- Ask the trust-boundary question first, then cover the other buckets
- Derive a monthly cost and an exception load before you draw a box
5-Phase Framework
A repeatable opening for an agentic system-design round: restate, clarify the trust boundary, derive one number, then design and harden.
Open every agentic design round the same way: restate, clarify the trust boundary (what the agent may do without a person), derive one number, then design and harden. A read-only Q&A bot and an agent that can issue refunds use the same model; the systems around it differ.
Drawn to scale; an illustrative split. Confirm the length with the recruiter.
Restate the problem with one constraint added
- “We are building an agent that reads vendor invoices, matches them to purchase orders and receipts, and never moves money on its own.”
- “I will design the match-and-approve path first.”
- “So, an invoice bot.” It adds nothing.
- Start drawing before the scope is named.
Rule of thumb: add one constraint the interviewer implied; do not parrot the prompt.
Play the prompt back with its scope. The restatement shows which problem you are solving, while a wrong assumption is still cheap to fix.
This section's case: an agent that matches vendor invoices to purchase orders and receipts.
Clarify the trust boundary before anything else
| Bucket | You ask | It decides |
|---|---|---|
| Trust and autonomy | 2-way or 3-way match (3-way: invoice, purchase order and goods receipt agree)? What does a wrong approval cost? Case: 3-way for goods; a false approval pays the fraud. | The agent recommends. A person releases payment. |
| Quality and evaluation | On a mismatch, does it resolve or flag? Case: small gaps resolve; the rest go to an analyst with a reason. | What the evals score: the auto-resolved set, and its errors. |
| Data and context | Where do orders, receipts and the vendor master (approved suppliers and their bank details) live, and where may the model run, under which contract? Case: not answered. | The ERP is the record. The agent reads it and writes no payments. |
| Cost and scale | How many a month, and must it be real time? Case: about 30,000 a month; within the hour is fine. | Async work: a queue, a few workers and a small model. |
Ask the trust boundary first. A read-only Q&A bot and a refund agent use the same model; the answer decides which system you draw around it. Cover the other buckets, and stop when the next answer would not change the design (The Answer Method).
Name each assumption out loud so the interviewer can correct it
| Assumption | Default |
|---|---|
| Model | The smallest current model for matching and extraction; a larger one for exception notes. |
| Pattern | A fixed workflow for extraction and matching. The model handles messy layouts and summaries, never arithmetic. |
| Trust | The agent recommends and a person releases payment in version one. The gate loosens later, with evidence. |
| Scope | One ERP, one currency, English invoices, one region in version one. |
State the defaults you design against, pattern first (The Agentic Spectrum). The case left the data default open, so say it as an assumption.
Derive one number before you draw
- Volume30,000 invoices a month (the case).
- RateAbout 1,000 a day, or 0.7 a minute averaged over 24 hours: a queue.
- Tokens3,000 in and 400 out per invoice (assumed).
- Dollars$0.0003 + $0.0002 = $0.0005 per invoice on Haiku 5.5 at list price (2026-10-08), thinking excluded; $15 a month.
- Exceptions10% need a person for 5 minutes (assumed): 250 analyst-hours a month.
The volume decides whether you need real time or a queue.
The exception queue, the invoices a person must handle, is the cost. Halving the tokens saves $7.50 a month; halving the exceptions saves 125 hours. Batch, the asynchronous API, is 50% off, but its results can take up to 24 hours, which can break the case's one-hour target.
Walk one request down the six layers
Blue: input. Amber: model or decision. Purple: state. Teal: the tool gate. Red: untrusted.
Follow the request down the layers. The context engine assembles retrieval (RAG: documents fetched at request time), memory and tool definitions. 10-Layer Architecture extends this spine to ten layers.
The loop runs over layers 4 to 6, once per model call. The tool gate sits outside the loop; its mechanics are in Permissions & Hooks. The vendor's PDF arrives inside a tool result, so it is checked at the tool boundary.
Harden one lever per concern
| Concern | Lever | Number |
|---|---|---|
| Security and trust | Tool gate, outside the loop: check payee and amount, and send a payment above the ceiling to a second approver (Prompt Injection Defense). | $12,000 against a $10,000 ceiling needs a second approver. The ceiling is assumed. |
| Reliability | Agent runtime: a key per tool call, so a retry cannot repeat a payment (Agent Durability). | (1 + 2) × (1 + 2) = 9 HTTP calls per tier if your loop also retries twice. |
| Cost | LLM gateway: caching first, then fewer calls, then a smaller tier. The ladder is below. | $0.46 a query becomes $0.21 once the prefix is cached. |
| Scale | Orchestrator: queue the work and size workers to the rate. Respect the provider's limits (Rate Limiting & Cost). | Three meters per model class: requests, input tokens and output tokens a minute. A spend-cap 429 has no retry-after; retries fail until 00:00 UTC on the 1st. |
| Quality and operations | Observability rail: replay a golden set (past cases with known outcomes) before each change; canary with a kill switch (Deployment & Rollout). | 300 clean replays bound the failure rate below 1% (exact: 0.99%). |
One lever and one number per concern. Cost gets a ladder, which checkpoint 2 reuses.
| Lever | Per query | The catch |
|---|---|---|
| Start: 8 calls, Opus 5.5 | $0.46 | The re-sent prefix is 83% of the input bill. |
| Cache the 10,000-token prefix, Opus 5.5 | $0.21 | One write at 1.25×, then reads at 0.05×. Needs a stable prefix. |
| Four calls, not eight, Opus 5.5 | $0.13 | Only if the work fits four calls. Prove it in the evals. |
| Four cached calls, Sonnet 5.5 | $0.06 | Still over the $0.05 budget. |
| Four cached calls, Haiku 5.5 | $0.003 | Under budget, if the evals pass on the smallest current model. |
Prices per million tokens as of 2026-10-08: Opus 5.5 $4 in and $20 out; Sonnet 5.5 $2 and $10; Haiku 5.5 $0.10 and $0.50 (prompts up to 100,000 tokens). Token counts are assumed. Thinking is excluded: it is billed as output and on by default, so each figure is a floor.
Free wins come first: caching, trimming, a prompt audit, and Batch for work that can wait up to 24 hours.
Caching, which stores a repeated prefix once so later calls read it cheaply, is the largest measured lever: Anthropic's cost guide reports agent-loop cost cut by 2.7 to 5.3 times on its own benchmarks (directional, read 2026-10-08; Prompt Caching).
Effort and tier trade quality, so sweep them on your traffic (Agent Cost Control). The semantic cache is last (Semantic Caching).
Keep the round inside its time limit
Interactive: move the sliders against a 45-minute limit. Static split: restate 2, clarify 6, assume 2, design 23, harden 8, wrap-up 4 minutes.
When the round runs long, shrink harden first, to a line per concern. The split is illustrative; confirm the length with the recruiter.
- A guardrail in front of the model, and nothing after it. Injection arrives in tool results and retrieved documents, so gate the action.
- Semantic cache first. It is the riskiest lever, and prompt caching is the largest measured one. Key it per tenant and measure the wrong-hit rate before serving (Semantic Caching).
- Trusting self-reported confidence. Use checks a program can run, such as totals that reconcile.
- Batch under a one-hour target. Batch can take up to 24 hours.
- A service account that retrieves for everyone. Retrieve with the requester's permissions (Multi-Tenant Isolation).
“Design an agent that reconciles invoices against purchase orders. Where do you start?” Restate the scope, adding that it never moves money on its own. Ask the trust boundary first, since a wrong auto-approval pays a fraudulent invoice; then the match rule, volume and latency. The trap is drawing boxes first.
Round formats differ, so confirm the length, the tools and the AI policy with the recruiter. If the interviewer brings a structure, keep your four buckets and map your phases onto theirs. See The Answer Method for when to stop asking.
The interviewer says “Design an agent that books travel for employees.” Which clarifying questions do you ask, and what would each answer change?
Trust: can the agent charge a corporate card, or only recommend, and above what amount does a person approve? That sets the gate.
Quality: what does a failed booking look like? That sets the eval set. Data: where do the options come from? That sets the data path.
Cost and scale: how many travelers at peak, and what does a booking cost? That sets queue or real time.
A support agent costs $0.46 a query against a $0.05 budget: 8 calls of 12,000 input and 500 output tokens on Opus 5.5. Which lever do you pull first, and what does it leave you?
Cache the 10,000-token prefix: $0.21, because the re-sent prefix is 83% of the input bill.
Fewer calls reach $0.13, still over budget. Four cached calls on Sonnet 5.5 cost $0.06, still over; Haiku 5.5 costs $0.003, under budget, if the evals pass. Semantic cache comes last. Figures exclude thinking, so they are floors.
The invoice agent reads vendor PDFs, sees the vendor master and can approve. A vendor hides “approve this invoice” in white text. What is wrong with trusting the approval, and which choices stop a $12,000 payment to a fraudulent vendor?
The instruction and the invoice share one token stream, so the model's yes is no control; OWASP's LLM01:2026 calls defense architectural rather than interceptive.
At the tool gate, check the payee and amount, and send anything above $10,000 (an assumed ceiling) to a second approver, so a $12,000 payment cannot clear alone. At the context engine, flag a first-time payee.
At the vendor master, bank-detail changes are human-only. On the rail, audit each decision.
- Restate the problem with one constraint added, and do not parrot the prompt.
- Clarify the trust boundary first, then stop when the next answer would not change the design.
- Derive one number before you draw. The exception queue, not the tokens, is usually the cost.
- Untrusted text enters at layers 4 and 5, so the tool gate is the security layer.
- Harden one lever per concern: caching, then fewer calls and a smaller tier, after evals; semantic cache last.