Almavar by 2Prod / Solution brief 01
From intelligence to authorized action
Large offices run on many systems, so status and coordination still run on data calls. Almavar answers across every system with the asker’s own authority, and acts on the answer with their approval.
Product vision · 7 min read · October 2026
Figures: Almavar operating data, 30 June to 21 October 2026.
Status still runs on data calls
In a large office, each team runs its work in the tool that suits it. Engineering tracks work in Jira, service teams in ServiceNow, account teams in Salesforce, and a good share of status lives in email and Teams threads. Each team is well served. The leader responsible for all of them has no single place to look.
So status moves by data call. Leads are asked for updates, someone reconciles trackers and templates, and the results are assembled into a deck. Depending on the office, that takes from an hour to a week, and the picture is out of date by the time it is presented.
A data call. An operations leader emails six team leads: I need status from every team by Friday. Slides, please. All week the replies ask which tracker, which template, can you pull it again, while status_tracker_v12.xlsx and slide 33 of 38 circulate. The finished status table covers Mobile, Data, Platform, IT Service, Field Ops and Customer Success, stamped: data as of last Tuesday.
Dashboards shorten the cycle only while every source keeps the same fields. When a team renames a field or moves to a new tool, the feed breaks until someone rebuilds it. Many AI assistants don’t close the gap either: they can summarize a document someone hands them, but they can’t reach across systems with the right authority or act on what they find. The coordination still falls to people.
One question, every system
Almavar starts from the question, so no shared schema is needed. A leader asks in Teams or web chat what every team is working on and what is blocked. Almavar splits the question across specialist agents, one per system, which work in parallel and read each source as it is: the boards in Jira, requests and changes in ServiceNow, the pipeline in Salesforce, status mail in Microsoft 365. The answer comes back as a live view built for that question.
A leader asks in chat what every team is working on and what is blocked. Almavar replies that it is working as the leader and sends four specialists with the leader’s own access: Jira to read every team’s board, ServiceNow for requests and changes, Salesforce for the pipeline, Microsoft 365 for the status mail. The live view, Operations every team, as of today: 42 items in flight, 31 on track, 7 at risk, 4 blocked, broken down by team. Blockers: payments cutover on the Platform team, waiting on the vendor; store tablets in Field Ops, change not approved; warehouse refresh on the Data team, test data missing; Q4 renewals in Customer Success, 3 in legal review.
Because each specialist reads its system directly, a renamed field or a new tool doesn’t break the answer. In Almavar’s operating data, about 1 in 5 requests brought in specialist agents, and a typical call into a business system returned in about 0.4 seconds.
Governance comes before access
Every specialist in that example acts as the leader who asked. Before any read or write, Almavar settles who is asking, what each agent may do and whether a person must approve, then uses that person’s own sign-in in each system. Each system applies its own permissions, so the answer contains only what the leader could open, and the AI model never holds a credential.
Almavar governs every step. Inside the frame: a leader asks what every team is working on; their own sign-in is carried to each system; the agents read every team’s work only as that leader could; they propose only permitted actions, such as pausing one project, pushing another and telling the leads; the leader approves the exact change; the project, epics and messages are written under the leader’s own identity; every step is kept on the record. The rented AI model sits inside the frame and connects only to the policy gate. Every read and write to Jira, ServiceNow, Salesforce, Microsoft 365 and Teams passes through the policy gate, and every step is written to the append-only audit trail.
This answers two objections security teams raise about AI agents: a shared service account that can see everything, and a second permission model that someone has to build and audit. In Almavar’s operating data the rule held without exception:
100% of calls into business systems ran under the requesting person’s own identity
0 calls fell back to a shared or service account
Recurring questions become workflows
Status is a recurring question, and a leader can turn any answer into a workflow in the same conversation, such as a refresh every morning and a summary by email on Mondays.
A leader asks Almavar to make the status view a workflow, refreshed every morning and emailed on Mondays. Almavar saves the workflow Operations every team: every day at 07:00 it refreshes the board, and on Mondays at 08:00 it emails a summary. It runs as the leader, and the leader’s permissions apply.
The workflow runs on schedule as the person who created it, with that person’s permissions. It replays the steps that worked, and when a connected tool changes, it adapts and keeps running. The weekly data call becomes a standing view that is current every morning.
From insight to action, with one approval
A current picture matters most when priorities shift. When a launch moves up and capacity has to come from somewhere, the same conversation can weigh every team’s load against the new priority and propose what to pause, what to push and what must not slip.
A leader tells Almavar the EU launch moves up to Monday and asks what to pause to make room. Almavar checks every team’s load against the EU launch and proposes: pause the portal redesign on the Mobile team; push the warehouse refresh on the Data team by a sprint; all 12 customer commitments stay on track; the EU launch starts with 4 epics.
Nothing changes until a person approves it. Almavar turns the plan into one approval card that lists each change in plain words, the system it touches and the identity it runs under. At the press it re-checks the permission and runs exactly what was approved: the project in ServiceNow, the epics in Jira and the messages in Teams, each one recorded.
An Almavar approval card. The leader asks Almavar to apply the plan, tell the leads and track it. Needs your OK: set up the EU launch. Create the EU launch project in ServiceNow; create epics EU-1 to EU-4 in Jira; message the two affected leads in Teams; update the executive sponsor in Teams. Runs as you, with your own sign-in in each system. Approved: done as the leader, with every step on the record.
Approval protects you only if people keep doing it, so it has to be fast:
How people answered 825 approval requests: 77% approved as proposed, 16.1% declined, 6.9% approved and then failed in the target system and reported as failed. Median time to approve: 17 seconds. 59% of multi-step plans needed no extra approval because the request itself named the work.
People approved 77% of proposals as written and declined 16%, so the card catches the changes a person disagrees with. When a request already names the work, Almavar treats it as consent for that plan, which removed the extra approval from 59% of multi-step plans. Sends, deletes, payments and identity changes always get a card.
Oversight that scales
Most of what agents do never needs a person. Routine reads and updates run under policy, and Almavar stops for the changes that need a decision:
Before it runs, every action is checked for who is asking, what the agent may do, what policy allows and whether a person must approve. Of 18,098 agent actions, 89.5% ran under policy without interrupting anyone, 8.5% waited for a person’s approval and 2.0% were stopped.
About 9 in 10 actions ran under policy, about 1 in 12 waited for a person, and 2 in 100 were stopped. In the example above, the leader’s weekly data call is replaced by a view that refreshes at 07:00, and every change made on the leader’s behalf traces back to one approval.
Where these figures come from.
Figures come from Almavar’s operating deployment between 30 June and 21 October 2026, which 2Prod runs for its own company work.
Put a real workflow on the table.
Which systems matter, what should the agent do, who may approve it, and where must it run?