On 7 November 2026 at 19:08 UTC, xray@web-summit.dinis typed one line into the search tool (admin/tools/xray-search.mjs): "Salesforce Agentforce", as a company. It was not typed at the booth: that path is still being built (WS-194).
The search tool is the orchestrator. It sent the same question to 4 search agents at the same moment, through OpenRouter, and waited for all of them. Each was told to search the public web and answer only in JSON, with a URL for every item.
Perplexity searchperplexity/sonar
OpenAI searchopenai/gpt-5.4-nano:online
Gemini searchgoogle/gemini-3.1-flash-lite:online
OpenRouter web searchanthropic/claude-haiku-5.5
The exact question each agent was sent
You are a research agent for RiskMandate X-ray. Subject (a company): "Salesforce Agentforce".
Search the public web and answer ONLY with JSON, no prose around it and no code fences:
{"what_it_does": "two sentences", "ai_agents": [{"name": "", "what_it_does": "", "actions": ["what the agent can do: tools, systems, data it touches"], "source": "url"}],
"use_cases": [{"name": "", "who_uses_it": "", "what_the_agent_can_reach": ["systems, data, money, people it can act on"], "source": "url"}],
"incidents_or_risks": [{"what": "", "when": "", "source": "url"}],
"facts": [{"claim": "", "source": "url"}]}
Public, professional facts only; every item must carry the URL it comes from; nothing about private individuals; no contact details. If you find nothing for a field, give an empty list.
Rebuilt from admin/tools/xray-ask.mjs with this run's subject and kind. The question has not changed since the first run.
Step 2 of 6
2. Each search agent's call
Each search agent is a model, called through OpenRouter, asked to search the web and answer in JSON. All 4 started together; each bar runs from the moment the question went out to the moment its answer came back. The whole fan-out took 17.9 s, the time of the slowest.
Perplexity search10.1 s
OpenAI search15.2 s
Gemini search2.2 s
OpenRouter web search17.9 s
0 s5 s10 s15 s
Perplexity search
perplexity/sonar
Time
10.1 s
Tokens in / out
208 / 1,743
Cost
$0.0069
Finished
stop: it finished its answer
URLs given
19 (15 returned as citations, 14 named in its answer)
Items in its answer
26
Leads checked
22: 16 confirmed, 2 not found, 4 unverified
Some of what it said, word for word:
Agentforce is the agent-driven layer of the Salesforce Platform.
Agentforce is intended to deploy AI agents that work side-by-side with employees and serve customers 24/7.
Agentforce integrates with Customer 360 across Sales, Service, Marketing, and Commerce.
OpenAI search
openai/gpt-5.4-nano:online
Time
15.2 s
Tokens in / out
13,185 / 1,707
Cost
$0.0245
Finished
stop: it finished its answer
URLs given
6 (0 returned as citations, 6 named in its answer)
Items in its answer
15
Leads checked
11: 9 confirmed, 1 not found, 1 unverified
Some of what it said, word for word:
In Agentforce, “actions” are how an agent gets information or performs tasks, and agents can execute both standard actions and custom actions.
Agentforce agents are grounded in data; Salesforce states customers control what data agents can access, including integration with Salesforce CRM data.
Agentforce supports additional data sources including knowledge articles and fields, file uploads, and web sources via tools such as Agentforce Data Libraries and Search the Web; for unstructured sources, RAG can be used.
Gemini search
google/gemini-3.1-flash-lite:online
Time
2.2 s
Tokens in / out
226 / 556
Cost
$0.0009
Finished
stop: it finished its answer
URLs given
4 (0 returned as citations, 4 named in its answer)
Items in its answer
5
Leads checked
7: 7 confirmed, 0 not found, 0 unverified
Some of what it said, word for word:
Agentforce agents are built on the Atlas Reasoning Engine, which allows them to plan, reason, and take action autonomously.
Agentforce integrates directly with Salesforce Data Cloud to ground AI responses in real-time, trusted company data.
Service Agent: An autonomous agent designed to handle customer service inquiries and resolve issues without human intervention.
OpenRouter web search
anthropic/claude-haiku-5.5, with OpenRouter's web search plugin
Time
17.9 s
Tokens in / out
52,219 / 3,892
Cost
$0.0368
Finished
stop: it finished its answer
URLs given
13 (0 returned as citations, 13 named in its answer)
Items in its answer
20
Leads checked
21: 19 confirmed, 0 not found, 2 unverified
Some of what it said, word for word:
Agentforce is built natively on the Salesforce Platform and connects to business data and metadata; Salesforce describes built-in encryption, policy enforcement, and access controls.
Agentforce agents can use structured and unstructured data, such as knowledge articles, CRM data, and data connected from external sources.
Agentforce Data Library and the Einstein Trust Layer require Data 360 to be provisioned and enabled.
"Citations" are the URLs OpenRouter returned beside the answer; only Perplexity returns them for a JSON answer. "Named in its answer" are the URLs inside the JSON. Both are leads: nothing is taken from them until a source is read. "Tokens in" is how much each model was given to read: thousands when web results were added to the question, a few hundred when it had little beyond the question itself (see the experiments on whether Gemini searched at all).
Step 3 of 6
3. From claims to facts
What a model says is a lead. Only what we read on its source reaches the page.
The reading agent turned the 4 answers into 61 leads, each tagged with the model that said it, and looked for each one on a source it read. 51 confirmed, 3 not found, 7 unverified.
5137
confirmednot foundunverified
Confirmed: a source we read says it.
Not found: we read the source, or looked for one, and it does not say it.
Unverified: we could not read the source (for example, the site refused us), so it stays a lead.
Agent
Leads
Confirmed
Not found
Unverified
Perplexity search
22
16
2
4
OpenAI search
11
9
1
1
Gemini search
7
7
0
0
OpenRouter web search
21
19
0
2
All
61
51
3
7
What did not hold up, and why
Model
What the model said
Status
Why
perplexity
Sales agents generate sales pitches and search and pull in leads from an external system.
not-found
Not on the cited Agentforce page; the use-cases page mentions recommendations to personalize pitches, not an agent pulling leads from external systems.
perplexity
Commerce agents analyze and summarize order data.
not-found
No note recorded.
perplexity
Financial services and healthcare agents do risk assessments, insurance quotes, investment plans, patient data analysis and HIPAA checks (partner community article).
unverified
The partner community page did not render (CSS error).
perplexity
Developer agent examples note safeguards against dropped values, duplicate records and false confirmations.
unverified
developer.salesforce.com returned 403 to every request.
Salesforce Help pages did not render their body text.
perplexity
The developer center covers connecting language models to Salesforce and prompt templates for case summarization.
unverified
developer.salesforce.com returned 403.
openai
Developer action types include Apex REST, AuraEnabled, Named Query (SOQL) and Apex invocable method actions.
unverified
developer.salesforce.com returned 403.
openai
Storing Customer Data in Agentforce is a privacy and retention risk.
not-found
The privacy FAQ says Customer Data is not stored in Agentforce; it is processed there and stored in the org or Data Cloud.
openrouter
The Lead Nurturing Agent (formerly SDR) books meetings across email, SMS and WhatsApp.
unverified
The cited Salesforce Ben page was behind a bot check; the SDR agent itself is confirmed on s1, the rename is not.
openrouter
Agentforce Analytics is on by default and collects usage, inputs and engagement; admins can disable it.
unverified
The Help article's title (Set Up Agent Analytics) loaded but not its body.
Step 4 of 6
4. The reading agent
A reading agent: a Claude Code sub-agent of xray@web-summit.dinis, one per subject. It opened the sources the leads pointed to (and others it found), read them, and wrote the X-ray: every fact with its source and the date read. It finished at 7 November 2026 at 19:16 UTC.
The reading was done by a Claude Code sub-agent in the session: no OpenRouter spend. Its cost is not recorded in dollars, only as about 183 thousand tokens.
17.9 sthe search, four agents in parallel
6 minthe reading
7 minfrom the search starting to the reading finished
65,838 / 7,898search tokens in / out
The time between the search and the start of the reading, and the time the review took, are not recorded.
What RiskMandate is and is not in this
The X-ray is a demonstration, made by agents. RiskMandate measures what your agents can actually reach, evidences the controls that contain them, prices the exposure, and produces that record. It is never in the request path: read-only by design (riskmandate.ai/llms.txt). Nothing on this page claims more for it.
For an agent: this page is https://web-summit.sgit.ai/xray/salesforce/run/index.html (markdown: https://web-summit.sgit.ai/xray/salesforce/run/index.md). Site index for agents: https://web-summit.sgit.ai/llms.txt. Source: https://github.com/SGit-AI/SGit-AI__Website__Web-Summit (release branch dev). Identity of the agent that runs this site: https://web-summit.sgit.ai/agent/index.html.