On 7 November 2026 at 19:08 UTC, xray@web-summit.dinis typed one line into the search tool (admin/tools/xray-search.mjs): "Atlassian Rovo agents", 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): "Atlassian Rovo agents".
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 28.0 s, the time of the slowest.
Perplexity search6.6 s
OpenAI search21.5 s
Gemini search4.4 s
OpenRouter web search28.0 s
0 s10 s20 s
Perplexity search
perplexity/sonar
Time
6.6 s
Tokens in / out
209 / 1,025
Cost
$0.0062
Finished
stop: it finished its answer
URLs given
15 (15 returned as citations, 9 named in its answer)
Items in its answer
16
Leads checked
17: 17 confirmed, 0 not found, 0 unverified
Some of what it said, word for word:
Rovo is Atlassian’s AI offering for finding personalized answers, automating tasks, and making smarter decisions using connected organizational context.
Rovo is powered by the Teamwork Graph, which maps relationships across people, apps, projects, code, decisions, and assets.
Rovo agents are designed to handle specific tasks and automate multi-step work across Atlassian and connected apps.
OpenAI search
openai/gpt-5.4-nano:online
Time
21.5 s
Tokens in / out
17,522 / 2,139
Cost
$0.0352
Finished
stop: it finished its answer
URLs given
9 (0 returned as citations, 9 named in its answer)
Items in its answer
13
Leads checked
14: 12 confirmed, 0 not found, 2 unverified
Some of what it said, word for word:
Rovo agents can be accessed in Chat, automation rules, within Jira/Confluence via shortcut commands, and in Studio.
Skills are reusable instructions that let Rovo Chat and agents carry out specific work-based actions (e.g., creating work items, drafting status updates, analyzing incident data).
Agents can access data from other Atlassian and connected third-party apps depending on their knowledge sources, and they respect user permissions (no cross-site access to unauthorized users).
Gemini search
google/gemini-3.1-flash-lite:online
Time
4.4 s
Tokens in / out
228 / 836
Cost
$0.0567
Finished
stop: it finished its answer
URLs given
4 (0 returned as citations, 4 named in its answer)
Items in its answer
7
Leads checked
9: 7 confirmed, 2 not found, 0 unverified
Some of what it said, word for word:
Atlassian Rovo is powered by the 'Teamwork Graph,' which maps relationships across people, apps, projects, and code to provide context-aware AI interactions.
Rovo agents are designed to inherit the access permissions of the human user, meaning they can only see and act upon data that the authenticated user is authorized to access.
ResearchAgent: An agent capable of conducting multi-source web research and navigating across arbitrary sites to synthesize information.
OpenRouter web search
anthropic/claude-haiku-5.5, with OpenRouter's web search plugin
Time
28.0 s
Tokens in / out
34,750 / 6,038
Cost
$0.0262
Finished
stop: it finished its answer
URLs given
13 (0 returned as citations, 13 named in its answer)
Items in its answer
28
Leads checked
28: 28 confirmed, 0 not found, 0 unverified
Some of what it said, word for word:
Rovo agents are described by Atlassian as configurable AI teammates that any team member can call on or create, and they draw on Atlassian apps and connected third-party apps.
Agent skills are the actions an agent is allowed to take, such as drafting a Confluence page or sending Slack messages, while knowledge is the data it can reference.
Atlassian's documentation says agents can be configured with knowledge sources such as Confluence, Jira, and Google Drive, plus deep research or web search.
"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 68 leads, each tagged with the model that said it, and looked for each one on a source it read. 64 confirmed, 2 not found, 2 unverified.
6422
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
17
17
0
0
OpenAI search
14
12
0
2
Gemini search
9
7
2
0
OpenRouter web search
28
28
0
0
All
68
64
2
2
What did not hold up, and why
Model
What the model said
Status
Why
openai
Admins can control an agent's knowledge scope, for example unit-wide scope versus specific sites.
unverified
The cited Atlassian Community discussion returned an empty page; the knowledge sources page (s8) describes builder-chosen custom knowledge, not this admin control.
openai
A Reddit discussion reports a critical cross-user and cross-tenant compromise in Rovo.
unverified
The Reddit page returned no content; no other source read today reports a cross-tenant failure, and the THN and CSA reports describe exposure bounded by the signed-in user's own access.
gemini
Engineering use case reaches source code in Bitbucket or GitHub and incident alerts in Opsgenie.
not-found
The cited Rovo page (s1) mentions pulling context across code but names neither Bitbucket, GitHub nor Opsgenie.
gemini
IT service management use case reaches JSM tickets, knowledge base articles, incident history and SLA status.
not-found
Not on the cited Rovo page (s1); JSM agents are documented on s5, but SLA status access is not stated anywhere read.
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 19:18 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 204 thousand tokens.
28.0 sthe search, four agents in parallel
8 minthe reading
9 minfrom the search starting to the reading finished
52,709 / 10,038search 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/atlassian/run/index.html (markdown: https://web-summit.sgit.ai/xray/atlassian/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.