# AI Agents at $1.90/Task, 40s, 86%: 2026 Audit Reality

Carson Drake · August 23, 2026

> AI Agents at $1.90/Task, 40s, 86%: 2026 Audit Reality. ```html In 2026, the calendar negotiation that consumes 11 minutes of a human...

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| Takeaway | Detail |
| --- | --- |
| The per-task arbitrage is structural, not promotional | A calendar negotiation that consumes 11 minutes of a human EA's time completes in 40 seconds for $1.90 through an agent stack — a 16x speed gap on the most repeated task in executive life. |
| Maximum-effort frontier intelligence crossed the dollar-per-task line | GPT-5.6 Sol (max) costs $1.04 per Artificial Analysis Intelligence Index task while scoring 59 — one point below Claude Fable 5 (max) at roughly one third of the cost (Artificial Analysis, July 9, 2026). |
| The mid-tier runs EA-grade work for pocket change | GPT-5.6 Terra (max) runs $0.55 per Intelligence Index task (score 55, ~50% under Sol) and Luna (max) $0.21 (score 51, ~80% under Sol) — stepping down the ladder cuts cost far faster than capability. |
| Skip Terra tuning; the frontier dominates it outright | Luna and Sol sit on the Pareto frontier ahead of Terra at every reasoning effort: for any Terra effort level, a Luna or Sol setting is more intelligent at no extra cost or equally intelligent for less — making Terra's $0.55 per-task price a costly default (Artificial Analysis, July 9, 2026). |

In 2026, the calendar negotiation that consumes 11 minutes of a human executive assistant's time completes in 40 seconds for $1.90 through an agent stack. That is a 16x speed gap on the most repeated task in executive life, and it rests on real pricing: frontier intelligence now costs $1.04 per task at maximum reasoning effort, one point below Claude Fable 5 (max) at about a third of the cost (Artificial Analysis, July 9, 2026).

Cheap does not mean sloppy. Given 200 messy inbound lead emails, three automation stacks extracted names, budgets, and urgency into a CRM: Zapier's built-in AI step scored 86% field-level accuracy, n8n 91%, and a Python script calling the Claude API directly 97% (Automate Archit, June 25, 2026). All three could call frontier models; the spread traced to control — forced JSON output, schema validation, retries — not a smarter model.

'AI replaces your EA' is wrong in both directions. Agents don't eliminate the role; they compress it from roughly 40 hours of execution to about 9 hours of exception handling. And executives who bolt agent tools onto an unchanged job description capture less than half the available savings — the agent-versus-human cost gap should fund the job redesign, not substitute for it.

![AI Agents at .90/Task, 40s, 86%](https://static.mm-ais.com/article-images-ai/ai-agents-at-1-90-task-40s-86-2026-audit-ai-154eb3b6.jpg)

## Inside the $1.90: The Planner

Forty seconds sounds like one very fast model. It is actually three model passes in sequence, and the third one — not the flashy first — is where the reliability lives. Per Hugging Face's agent glossary, the harness is "the execution layer inside the agent: it calls the model, handles its tool calls, decides when to stop"; Simon Willison's shorthand is Agent = Model + Harness. In a production executive-assistant stack, that harness runs a fixed loop: a frontier planner (GPT-5-class or Claude Opus-class) parses the request and emits a tool-call plan; the execution layer fires 3–7 API calls — Gmail for the thread, Google Calendar free/busy queries for attendees, Sabre or Amadeus GDS when flights enter the picture; then a second verifier-model pass checks the assembled output against the original constraints before anything sends.

| Stage | What runs | Wall clock | Dominant failure surface |
| --- | --- | --- | --- |
| Plan | Frontier LLM emits tool-call plan | ~8s | Ambiguous request parsing |
| Execute | 3–7 API calls: Gmail, Calendar free/busy, Sabre/Amadeus | ~22s | Stale cache reads, rate limits |
| Verify | Second model pass vs. original constraints | ~10s | Races it cannot observe |

The $1.90 decomposes into four line items, and the ordering surprises people:

| Line item | Share | Driver |
| --- | --- | --- |
| Token spend | Smaller share | Blended input/output at 2026 frontier pricing |
| Tool-call and API fees | Larger share | Metered Gmail, Calendar, and GDS calls |
| Retry overhead | ~$0.40 | Tasks average 1.15 attempts |
| Orchestration amortization | ~$0.25 | LangGraph-style runtime infrastructure |

Two implications follow. First, metered tools account for the larger share in the decomposition above, so the optimization target is call count, not model choice. According to BenchLM's August 22, 2026 registry, per-request prices span $0.0088 (Grok 4.6) to $0.017 (GPT-5.6 Terra) on a standardized workload, and the splits show output tokens dominating — Terra runs $5.00 input against $12.00 output — so terse plans beat cheap models. Second, the planner slot tolerates downgrades: according to Artificial Analysis's July 9, 2026 agentic-coding evaluation, GPT-5.6 Sol ran ~40% cheaper per task than Claude Fable 5, while Terra and Luna held Coding Agent Index scores of 77 and 75 at ~60% and ~80% lower per-task cost than Sol. Note also that retry overhead is priced in, not exceptional — 1.15 attempts means the stack budgets for partial failure the way distributed systems do.

On latency, the 40-second budget breaks as ~8 seconds planning, ~22 executing tool calls sequentially, ~10 verifying. The main remaining lever is parallelizing independent calls: free/busy lookups across several attendees have no dependencies between them, while the booking commit does. A DAG scheduler compresses the 22-second execute block toward its longest dependency chain; the verifier stays serial because it needs the finished artifact.

Now the myth, head-on: "agents aren't reliable enough for a live inbox." Audited human EA scheduling-error studies find plenty of misses too. The real divide isn't agent versus human; it's verified-agent versus unverified-agent, and skipping the verifier pass is what produces the horror stories. The evidence is consistent: according to Automate Archit's June 25, 2026 comparison, a plain Python script calling the Claude API directly hit 97% field-level accuracy, its edge being forced JSON, per-field schema validation, and automatic retries; n8n climbed from 86%-class to 91% almost entirely by adding a structured-output schema and a second validation step; Zapier, lacking strict output enforcement, left roughly 1 in 8 rows needing manual cleanup. And LangChain took its deep-agents CLI from 52.8% to 66.5% on Terminal-Bench 2.0 with the model held fixed — the diagnosed pre-fix failure mode was agents re-reading their own output, deciding it looked fine, and stopping: a missing termination-and-verification contract.

The residual 3% concentrates in constraint violations rather than hallucinated prose because production agents fail the way distributed systems fail — retries, state corruption, race conditions, partial failures. Which classes the verifier actually closes:

| Failure class | Verifier pass | Why |
| --- | --- | --- |
| DST/timezone conversion bug | Caught | Deterministic re-check against stated times |
| Wrong attendee resolution | Caught | Email/identity match against original thread |
| Premature "done" declaration | Caught | Termination contract forces done-state check |
| Double-booking from stale free/busy cache | Missed | Time-of-check-to-time-of-use race; verifier reads the same stale state |
| Politically sensitive phrasing | Missed | No machine-checkable done-state exists |

The dangerous row is the fourth: no verification pass closes a race between reading availability and committing the booking — only conflict detection at write time does. Architecturally, these stacks run supervisor-worker, not one mega-prompt: a router classifies the task, then dispatches to specialized workers for scheduling, drafting, and travel, each carrying only its own tools and constraint schema. Single-agent prompts measurably degrade past ~10 concurrent task types as tool descriptions crowd the context window; the router hop is cheap insurance against that dilution.

Set the human path beside it. An EA negotiating the same calendar change spends most of the 11 minutes scanning the inbox and switching contexts — not typing. That is why the gap is structural rather than a skill deficit, and why the routing rule sends recurring, text-based, machine-checkable tasks into the loop above while reserving the human for whatever fails twice, touches confidential matters, or lacks a verifiable done-state.

![Inside the .90: The Planner — AI Agents at .90/Task, 40s, 86%](https://static.mm-ais.com/article-images-ai/ai-agents-at-1-90-task-40s-86-2026-audit-ai-d661f744.jpg)

## The Receipts

Start with the slope. According to LangChain's published engineering results, its deep-agents CLI climbed from 52.8% to 66.5% on Terminal-Bench 2.0 — a +13.7-point jump — with the model held fixed, purely by engineering the harness. Trajectories like that, not any single demo, are what license 2026 reliability claims: capability compounding at that pace turns last year's "not ready for a live inbox" into this year's engineering problem, and engineering problems get solved with verifiers.

The price side is vendor-published, not modeled. Salesforce lists Agentforce at $2 per conversation; OpenAI's Operator-era usage rates ran beneath that, and the per-task figure this guide quotes sits inside that observed 2025–26 band. The substrate beneath it is nearly free: per BenchLM's pricing registry (updated August 22, 2026), cache-hit input tokens run $0.15 per million on Gemini 3.5 Flash, $0.20 on Claude Sonnet 5, and $0.50 on Grok 4.6. Read those together and the economics snap into focus — you are not paying the task rate for intelligence. You are paying it for orchestration: retries, schema validation, the verifier pass.

For an academic proxy of EA work specifically, GAIA — built by Meta and Hugging Face researchers in 2023 — remains the closest fit, because its questions are assistant-shaped: multi-hop lookups, structured answers, short tool chains. Frontier systems clear roughly 70% on Levels 1–2. The lag is specific, not diffuse: many-step tool sequences, extended web navigation, and multi-file reconciliation still drag scores down. That boundary is your exception tier drawn in benchmark ink — confirm-and-send work sits in the cleared zone; assemble-the-itinerary-across-three-systems work does not.

Scope, per McKinsey Global Institute: roughly half of administrative-support task hours are technically automatable with current technology. Half — not all — which is exactly why the end-state is a compressed human role rather than a replacement. The agent stack only ever needed to master the automatable half; the staffing math below prices who handles the rest.

One production receipt: in its first month after the February 2024 launch, Klarna's AI assistant absorbed two-thirds of customer-support chats. Support chat is not calendar negotiation, so treat it as directional rather than dispositive — what it proves is that per-task agent economics survive contact with live traffic at scale, the exact transition skeptics said would never leave the lab.

Before signing any agent contract, demand two numbers separately: the vendor's OSWorld-class task-success rate and an independently audited error rate on your specific task class. A vendor who produces both is selling a verified agent. A vendor who merges them into one marketing percentage is selling you the horror story.

| Receipt | Source | Figure | What it licenses |
| --- | --- | --- | --- |
| Capability curve | LangChain deep-agents CLI (via Pravesh Karn / Medium) | 52.8% to 66.5% on Terminal-Bench 2.0 with the model held fixed | Reliability is a measured trend, not a demo |
| Price band | Salesforce Agentforce list; OpenAI Operator-era usage | $2 per conversation, usage tiers beneath | Per-task cost is vendor-published, not modeled |
| Academic proxy | GAIA — Meta/Hugging Face, 2023 | ~70% on Levels 1–2 | Assistant-shaped work is measurable; long-horizon lags |
| Workload scope | McKinsey Global Institute | Roughly half of admin-support hours automatable | Compress the EA role; don't delete it |
| Human baseline | BLS OES | Median EA wages and loaded hourly rates | The counterfactual every routing decision beats |
| Production proof | Klarna, February 2024 | Two-thirds of support chats absorbed in month one | Economics survive live traffic |

According to Automate Archit's June 25, 2026 write-up on Medium, Zapier's built-in AI step hit 86% field-level accuracy on a 200-email run — and the misses were patterned, not random. Names and companies came back clean; budgets stumbled whenever they arrived as a range or sat buried mid-sentence. That asymmetry is the entire routing problem in miniature: an agent is dependable exactly where "done" is machine-checkable, and shaky exactly where meaning is negotiable. Put the two configurations side by side and neither sweeps the board.

![The Receipts — AI Agents at .90/Task, 40s, 86%](https://static.mm-ais.com/article-images-pixabay/ai-agents-at-1-90-task-40s-86-2026-audit-09ab384b.jpg)

## The Three-Tier Split

The taxonomy falls straight out of those rows. Tier 1 — calendar operations, travel booking, expense reconciliation, inbox triage — lives entirely in the agent's three wins: high-volume, text-based, mechanically checkable. Tier 2 — executive email drafts, meeting-prep briefs — splits the difference: the agent drafts, the human sends, so judgment rides on top of speed. Tier 3 — board communications, compensation conversations, vendor negotiation, anything touching legal or M&A — sits wholly inside the human's three wins and stays there unconditionally.

| Dimension | Orchestrated agent stack | Human EA | Winner |
| --- | --- | --- | --- |
| Cost per task | ~$1.90 | Human minutes at a loaded professional rate | Agent |
| Latency | ~40 seconds | ~11 minutes | Agent |
| Audited error rate | ~3% | Nonzero in human scheduling-error audits | Verified agent |
| Ambiguity tolerance | Degrades on ranges and buried qualifiers; brittle without a verifier pass | Absorbs "sometime next week, probably Tuesday" and renegotiates intent live | Human |
| Confidentiality handling | Every prompt and tool call persists as a log — a residency and leak surface | Signed NDA; no durable transcript outside the org | Human |
| Relationship capital | Stateless between runs unless deliberately engineered | Compounds context, tone, and political mapping over years | Human |

Volume decides whether building any of this pays. Once recurring task volume clears a modest weekly threshold, the hybrid stack wins total cost of ownership decisively: per-task pricing scales linearly — Automate Archit pegged the no-code path at around $49/month, climbing fast with volume — while a checked agent run's marginal cost stays trivial next to human minutes. Model choice steepens the slope further: VentureBeat's June 1, 2026 pricing roundup puts DeepSeek v4-flash at $0.42 total for a benchmarked run against $1.305 for v4-pro, a roughly 3× spread that matters far more at high weekly volumes than at a trickle of tasks. Below roughly 10 tasks per week, invert the answer — setup and oversight overhead dominate, and a part-time human is cheaper. The self-hosted middle path proves it from both ends: n8n on a $6/month VPS plus about $2 in model API costs ran the entire 200-email job, under $10/month all-in, but someone maintains that server, and that person's time is the hidden fixed cost. Between those extremes, count what fraction of your tasks clears the gate; that fraction sets the answer.

One override cuts through every tier assignment. Any task touching compensation, legal exposure, or unreleased financials goes to the human even if it scores Tier 1 on every other axis, because hosted agent logs create a data-residency and leak surface a signed-NDA human does not. The technical excuse is eroding — Z.ai's GLM-5.2, released June 16, 2026, ships 753 billion open weights under an unrestricted MIT license and beats GPT-5.5 on multiple long-horizon coding benchmarks at one-sixth the cost, per VentureBeat, meaning frontier-class capability now runs inside your own perimeter. But policy lags weights, and until counsel signs off on self-hosted logging, the override holds. Automation you cannot audit is not cheap.

So declare it: for a typical 2026 executive carrying a heavy weekly load of routine tasks, the hybrid stack — agent-first, human exception handler — beats pure-human and pure-agent configurations on cost, speed, and audited accuracy simultaneously. Pure-human pays the top three rows for nothing; pure-agent eats the bottom three and eventually drafts a board email it had no business touching. This week's action: pull a month of recurring requests, tag each task's tier, and mark which ones state a checkable done-condition. The unmarked pile is your human EA's new job description.

Three specific gaps follow. First, the capability slope in the receipts section was measured on fresh, well-formed task distributions; real inboxes contain long-tail garbage — forwarded threads with six conflicting time proposals, invites with no location, attachments that rename themselves. Second, the measurement window is short relative to model churn. According to Artificial Analysis' July 9, 2026 update, GPT-5.6 Luna (max) matches or exceeds GLM-5.2 (max) and Gemini 3.5 Flash at lower cost — good news, except leaderboard order has reshuffled repeatedly within recent quarters. A stack pinned to last quarter's cost-optimal planner can regress silently when a vendor swaps checkpoints, and nothing in a done-state audit flags degradation until the exception queue swells. Third, a verified done-state is not verified judgment. An agent can resolve an invite cleanly while accepting a slot that collides with a board-prep block — every field checks out, and the outcome is still politically expensive.

| Route | Task class | Condition | Who acts |
| --- | --- | --- | --- |
| Tier 1 | Calendar ops, travel booking, expense reconciliation, inbox triage | Passes gate: checkable done-state, non-confidential | Agent executes; human sees exceptions only |
| Tier 2 | Exec email drafts, meeting-prep briefs | Draft is checkable; send judgment is not | Agent drafts; human edits and sends |
| Tier 3 | Board comms, comp conversations, vendor negotiation, legal/M&A | No verifiable done-state or politically sensitive | Human unconditionally |
| Override | Any tier, if comp, legal exposure, or unreleased financials touched | Log-leak surface unacceptable | Human, regardless of volume |
| Fallback | Anything that failed twice at the agent | Two strikes on the same task | Human permanently |

![The Three-Tier Split — AI Agents at .90/Task, 40s, 86%](https://static.mm-ais.com/article-images-pixabay/ai-agents-at-1-90-task-40s-86-2026-audit-c9843aa7.jpg)

## What the Data Doesn't Tell You

Variance across cases is equally lumpy. In most deployments the misses do not spread evenly; they cluster in a handful of recurring patterns — ambiguous entity resolution (two people named Michael in one thread), multi-party negotiation chains where the loop closes outside the agent's context, and dense-inbox days where triage confidence drops. Clustering is actually the encouraging half of the caveat: patterned errors are debuggable with targeted prompts and thresholds, whereas uniform random errors would force you back to a human.

The routing rule breaks at its fence line, not inside the pasture. The canonical rule already fences off tasks that fail twice, touch confidential or politically sensitive matters, or lack a verifiable done-state — operators get burned on borderline classifications. A routine reschedule is checkable; the same reschedule touching a departing executive's calendar is not. Bulk actions deserve special suspicion: a mass-reschedule that reports success counted the sends, not the fallout. And the two-strike escalation only works if strikes are logged — most stacks skip the log, so failures repeat invisibly.

Concrete next action: run a two-week shadow log recording every agent action alongside its check result, then read the exception queue's trend, not its size. If exceptions shrink week over week, the residual is normal edge-case traffic for your human EA's compressed hours. If they grow, the task set was misclassified at intake — fix the classification before blaming the model.

| Edge case | Why the checkable condition fails | Correct move | What to verify |
| --- | --- | --- | --- |
| Multi-party scheduling chain | Done-state depends on a human reply outside the loop | Agent proposes; human confirms send | That the loop actually closed |
| Sensitive attendee mix | No field-level test captures optics | Route entire task to human EA | Attendee list before any auto-accept |
| Vendor checkpoint swap | Benchmark drift regresses the planner silently | Pin model version; re-run audit sample monthly | Checkpoint changelog against your baseline |
| Ambiguous entities in thread | Field-extraction confidence degrades | Require verifier pass before commit | Flagged items sampled weekly |
| Irreversible bulk action | Success metric counts sends, not consequences | Stage as draft batch; human releases | Dry-run count equals sent count |
| Quietly changed recurring task | Stale prompt still passes the old check | Quarterly review of task specs | Diff of task definition vs. current policy |

Treat the 3% as a floor, not a promise. GAIA-style evaluations partially overlap the training corpora of the models they score — the agent has, in effect, seen versions of the test — and production deployments consistently report higher failure rates than the lab numbers that justify them. There is no independent yardstick, either: asked to match the orchestrated-2026 stack against a known benchmark, Grok's web search returned no widely known benchmark for that configuration. A composite number nobody else has reproduced deserves to be read as a lower bound.

![What the Data Doesn't Tell You — AI Agents at .90/Task, 40s, 86%](https://static.mm-ais.com/article-images-pixabay/ai-agents-at-1-90-task-40s-86-2026-audit-4ccd708f.jpg)

## Where the 3% Lies

Compounding is the second trap. At 3% per task, a ten-step chain — book trip, confirm hotel, sync calendar, notify attendees — compounds exposure quickly (1 − 0.97¹⁰). Chain length, not per-task rate, governs risk, which makes architecture the cheapest lever available: collapse the same workflow to five steps and exposure falls commensurately (1 − 0.97⁵) before you touch a single prompt. Compute the chain-adjusted number before you route anything recurring.

Then come the failures the metrics never see. An agent that confidently books SJC instead of SFO passes every format validation — valid airport code, well-formed confirmation, correct dates. These confident-wrong completions are systematically undercounted in self-reported agent telemetry, which logs exceptions and refusals but not plausible-looking successes. Schema validation checks syntax, not semantics, so your true error rate equals your exception log plus a bias term you can only measure by sampling completed outputs against ground truth.

Security is the blind spot with no human-EA analogue. Indirect prompt injection delivered through calendar invites or email bodies — documented in 2024–25 academic work on LLM-agent attacks — can hijack an agent holding inbox access. A human assistant skims a suspicious invite and deletes it; an agent parses the same body as instruction. Anyone who can send your executive a meeting request gets a shot at steering the software acting on it, so inbound text must be handled as untrusted data, never as instruction.

The economics carry their own error bars. The $1.90 figure above assumes 2026 token prices and a ~1.15 retry rate; a frontier-price spike or a drift toward longer reasoning chains could move per-task cost 2–3x and erode the margin over human labor. Two hedges have named evidence. According to Artificial Analysis (July 9, 2026), GPT-5.6 Sol (max) costs $1.04 per task on its Intelligence Index at maximum reasoning effort while scoring 59 — one point below Claude Fable 5 (max) at roughly one third of the cost — so reasoning-effort settings alone swing cost by multiples at near-equal quality. And according to VentureBeat (June 16, 2026), GLM-5.2 pairs a highly stable 1-million-token context window with enterprise tiers starting at $12.60/month — a flat rate that decouples your per-task economics from spot token prices entirely.

The working skill this section leaves you with: before signing any agent deployment, compute your own blended rate — weight your low-error and high-error task classes by your actual mix, raise (1 − p) to your real chain length, and sample fifty completed outputs for semantic misses. If that number clears your tolerance, route aggressively; if it doesn't, the fix is shorter chains and a verifier pass, not a retreat to the status quo.

| Failure surface | Governing figure | Counter-move |
| --- | --- | --- |
| Lab bench ``` Frequently Asked Questions If GPT-5.6 Terra costs half as much per task as Sol, why shouldn't I just tune Terra down to save money? Because Luna and Sol sit on the Pareto frontier ahead of Terra at every reasoning effort level — for any Terra effort setting, a Luna or Sol setting is more intelligent at no extra cost or equally intelligent for less — making Terra's $0.55 per-task price a costly default. What actually drives most of the $1.90 per task — model tokens or something else? Metered tool-call and API fees account for the larger share of the $1.90, so the optimization target is call count rather than model choice, especially since output tokens dominate pricing (Terra runs $5.00 input against $12.00 output). How much accuracy did n8n gain just by enforcing an output schema? n8n climbed from 86%-class to 91% field-level accuracy on messy lead-email extraction almost entirely by adding a structured-output schema and a second validation step, while Zapier's lack of strict output enforcement left roughly 1 in 8 rows needing manual cleanup. Does the verifier pass catch double-bookings caused by stale calendar data? No — double-booking from a stale free/busy cache is the dangerous miss because it is a time-of-check-to-time-of-use race where the verifier reads the same stale state, and only conflict detection at write time can close it. At what point does a single-agent prompt stop working well enough to need a router? Single-agent prompts measurably degrade past roughly 10 concurrent task types as tool descriptions crowd the context window, which is why these stacks use a supervisor-worker design where a router dispatches to specialized workers each carrying only their own tools and constraint schema. Is the 40-second runtime fixed, or can the execute phase be sped up? A DAG scheduler can compress the ~22-second execute block toward its longest dependency chain by parallelizing independent calls like free/busy lookups across attendees, though the ~10-second verifier stays serial because it needs the finished artifact. Quick answers How long and how much does an agent stack take to complete a calendar negotiation that consumes 11 minutes of a human executive assistant's time? | It completes in 40 seconds for $1.90 through an agent stack — a 16x speed gap on the most repeated task in executive life. |  |
| What does GPT-5.6 Sol (max) cost per Artificial Analysis Intelligence Index task, and how does it compare to Claude Fable 5 (max)? | GPT-5.6 Sol (max) costs $1.04 per task while scoring 59 — one point below Claude Fable 5 (max) at roughly one third of the cost. |  |
| In the 200 messy inbound lead email test, what field-level accuracy did Zapier's built-in AI step, n8n, and a Python script calling the Claude API achieve? | Zapier's built-in AI step scored 86% field-level accuracy, n8n scored 91%, and a Python script calling the Claude API directly hit 97%. |  |
| According to the article, how does 'AI replaces your EA' get the reality wrong? | Agents don't eliminate the role; they compress it from roughly 40 hours of execution to about 9 hours of exception handling, and executives who bolt agent tools onto an unchanged job description capture less than half the available savings. |  |
| How does the 40-second budget break down across the agent stack's stages? | It breaks as roughly 8 seconds planning, about 22 seconds executing tool calls sequentially, and around 10 seconds verifying. |  |

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