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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteA scam-checking agent does not need to choose its own tools when every investigation follows the same checklist. In Steef-Jan Wiggers’ demo, the first version let the model orchestrate several evidence-gathering tools; after rate limits and an oversized conversation, he moved that fixed sequence into Python. The model then received one bounded evidence pack and interpreted it. The design lesson is conditional: make repeatable work explicit in code, but keep agent-led tool selection for tasks that genuinely need it.
What the scam-checking agent investigates
The demo answers a practical question: “Is that webshop legit?” It does not verify a shop with a single authoritative database. It gathers signals from several sources and asks the model to interpret them.
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- Domain registration: RDAP data, including registration date, registrar, and domain age.
- Website basics: whether the site responds over HTTPS and links to contact, about, terms, privacy, and returns pages.
- Archive history: Internet Archive CDX data to examine when the site first appeared and whether its history looks continuous.
- External reputation: optional Tavily searches for Trustpilot, general reviews, and scam or fraud reports. If a search key is not configured, this tier is marked unavailable rather than assigned an invented rating.
Wiggers says he chose not to scrape review sites or use individual review-provider APIs. In his account, Trustpilot’s content API required a paid business account, and scraping review sites would violate their terms. Those are the author’s stated reasons for this implementation, not a general legal analysis.
Why the first design ran into trouble
The original agent exposed four tools: check_domain, check_website, check_archive_history, and web_search. Its instructions asked the model to call them in sequence and run several searches. Wiggers reports that the flow first hit rate limits and then a context-window error.
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One Application Insights trace reported by Wiggers in 2026 recorded 47,004 input tokens and 280 output tokens. This is a single trace, not a benchmark. He attributes the large input to the conversation history being sent with each tool call: the system prompt, tool schemas, prior messages, and earlier results were replayed as the investigation continued. A missing Tavily key triggered retries and added to that history.
The underlying issue was a mismatch between orchestration and task shape. The evidence checklist was fixed, but the model was repeatedly asked to manage the sequence and carry the growing conversation needed to do so.
What changed in the second version
Wiggers replaced the four-tool sequence with one deterministic Python tool. That tool gathers registration, website, archive, and reputation-search evidence; the model calls it once and writes the assessment. In his words, the design is “evidence in code, judgment in the model.”
- Bounded output: the serialized evidence payload is capped at 8 KB, according to Wiggers’ description of the implementation.
- Visible failures: exceptions become short error fields instead of triggering retries.
- Unknown stays unknown: the instructions tell the model to treat missing signals as facts and not retry. For example, no configured search key means reputation evidence is unavailable; an archive timeout should remain unknown.
- Judgment remains separate: Python gathers the checklist evidence, while the model interprets it and writes the verdict.
This arrangement makes the tool’s work predictable and limits how much evidence enters the model’s context. It does not make the assessment itself authoritative: interpreting mixed evidence remains a judgment call.
What the example’s scores do—and do not—show
Wiggers reports a weekend-scale test on thenewsound.nl. The agent scored the site 68/100 without search grounding and 78/100 with Tavily grounding. In search results, he found a Trustpilot rating of 4.6/5 from 31 reviews; the shop itself claimed 4.8 from 890 reviews. The agent discounted the shop’s figure as self-published. A Wayback timeout was treated as unknown, and a commercial checker reportedly scored the same site 76/100.
All of those are observations from one example as reported by the author, not independently reproduced results or validated scam-detection performance. A score may help organize evidence, but it cannot establish that a shop is safe. Scammers can fake signals, and a legitimate new shop may have little archive history or few independent reviews.
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How to make a verdict useful rather than opaque
A number without its underlying evidence can hide the very uncertainty a shopper needs to see. The demo’s output lists raw signals, and Wiggers confirms that the judgment remains with the model. For a useful report, distinguish among positive, negative, and unknown evidence, and include the source URLs so a reader can inspect claims directly.
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- Show adverse findings, such as missing contact details or a concerning reputation result, without turning one signal into proof of fraud.
- Label unavailable data and timeouts as unknown; do not convert them into negative findings or reassuring results.
- Keep search-derived claims tied to the pages that produced them, and distinguish independent reviews from claims hosted by the shop.
When the evidence remains uncertain, use a payment method with buyer protection. The tool should help a shopper understand risk, not make a questionable site appear more legitimate than the evidence warrants.
When deterministic orchestration is the better fit
A fixed checklist is a strong candidate for explicit orchestration when each investigation needs the same sources, in the same general order. A deterministic tool can handle that routine once, bound the returned data, and report failures without inviting the model into an automatic retry loop. This also makes the evidence-gathering path easier to inspect.
That is not a case against agent loops in general. Dynamic tool selection and open-ended research can justify the extra orchestration when the next useful action depends on what has already been found. The question is whether the task truly needs the model to decide which tool comes next—or whether a known workflow can gather evidence more simply.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Azure Functions hosted skills provide
Microsoft documents Azure Functions hosted skills as a way to run AI-powered work in Azure Functions. A hosted skill is defined in an .agent.md file with YAML front matter for configuration and Markdown instructions; each hosted skill maps to one Azure Function. This is distinct from a reusable SKILL.md file, which is Markdown guidance rather than the hosted agent definition.
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Microsoft’s documentation describes triggers for HTTP requests, schedules, queues or messages, storage or database changes, and managed connector events. It also lists remote MCP servers, Azure connector namespaces, reusable skills, sandboxed Python execution through Azure Container Apps dynamic sessions, and custom Python tools as capabilities. The configuration reference allows one trigger per agent file.
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Microsoft labels hosted skills a preview. Its documentation warns that features, configuration names, and supported connectors can change before general availability. Treat the documented shape as a current preview snapshot, not a stable contract.
Choosing an Azure Functions hosting plan
Microsoft lists Flex Consumption, Premium, and Dedicated (App Service) plans for hosted skills. The best fit depends on scaling, latency, networking, execution duration, and whether an App Service plan is already available.
| Plan | Documented characteristics | Consider it when |
|---|---|---|
| Flex Consumption | Scale-to-zero, per-second billing, and automatic scaling. | Scale-to-zero behavior and automatic scaling fit the workload. |
| Premium | Pre-warmed instances, virtual networking, and unlimited execution duration. | You need pre-warmed capacity, virtual networking, or long-running execution. |
| Dedicated (App Service) | Always-on operation with manual or rule-based scaling. | An existing App Service plan or always-on hosting model suits the application. |
These are Microsoft’s documented plan characteristics; check the current reference before selecting a plan, because hosted skills remain in preview.
Provisioning and operational checks
Microsoft’s quickstart provisions a function app and related resources, including storage, monitoring, a model deployment, and a session pool. It can optionally provision Microsoft 365 Outlook connector resources for email. Microsoft warns that deploying the quickstart can incur Azure costs.
For a tool flow like the scam checker, make the operational checks part of the design:
- Keep search credentials optional only if the application can clearly report that reputation evidence is unavailable.
- Convert timeouts and exceptions into concise, typed evidence states rather than silently retrying.
- Cap tool output so a large search response cannot overwhelm the model’s context.
- Watch input-token telemetry in Application Insights, especially when tool calls may carry prior conversation history.
- Recheck Microsoft’s preview documentation before relying on configuration fields, connectors, or hosting behavior.
References: Steef-Jan Wiggers’ scam-checking agent account; Microsoft Learn: Azure Functions hosted skills; Microsoft Learn: hosted skills quickstart; Microsoft Learn: hosted skills reference.
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