One console. Every agent pipeline your operation runs.
Agent OS is the control plane we deploy on your infrastructure: the pipelines, the approval gates, the audit trail, and a dashboard that tells you the truth about what is running and what is not.
The approval queue. Every high-impact action stops here until a human clears it.
Tell us which pipeline
Two run today. Eleven more are blueprints we build to order. Pick one and we will tell you honestly which it is.
What the labels mean
Most vendors show you a deck. This is the same taxonomy our own console uses internally, and we publish it because the alternative is finding out on the first sales call.
Real backend, real data, running in production right now.
Real working software driven by sample business data.
Designed and specified in full. Built for you on a fixed scope.
Unified memory across every agent and every model
Most AI agents forget. Each one keeps its own context, that context dies with the session, and two agents working the same account never learn from each other. Swap the underlying model and whatever it knew goes with it. That is the normal failure mode, and it is why most agent pilots feel impressive once and disappointing thereafter.
Agent OS gives every agent one shared memory. Not a per-agent scratchpad — a single vector store that all of them read from and write to, regardless of which model is running them.
Every agent in the roster reads and writes the same store. What one learns, the rest can use.
Agents run on different LLMs from different vendors. The memory does not care which — swap a model and the knowledge stays.
Semantic recall, not keyword lookup. Backed by an append-only event log, so every recall traces to the event that wrote it.
Why this is the hard part
| Typical agent setup | Agent OS | |
|---|---|---|
| Memory scope | Per agent, per session | One store, every agent |
| Survives a restart | No — context dies with the session | Yes — persisted and re-queryable |
| Survives a model swap | No — knowledge is trapped in the model's context | Yes — memory sits outside the model |
| Cross-agent learning | None. Agents repeat each other's work | Shared. One agent's finding is available to all |
| Recall method | Whatever fits in the context window | Semantic vector search over the full history |
| Provenance | Usually none | Every entry carries instance, session, conversation and channel |
Figures describe the deployment running this console. Your instance is sized to your own workload, and the memory runs on your infrastructure alongside the orchestration layer.
Running in production today
Content Operations
An eleven-agent chain that researches, drafts, edits, internally links, compliance-checks and queues an article — then stops at a human approval gate.
This is the pipeline that produced the guides on this site. It runs daily against a keyword calendar with a semantic cannibalisation guard, and it does not publish anything a human has not cleared.
Lead Generation
Builds, enriches, deduplicates and verifies B2B prospect lists from public business sources into delivery-ready CSVs.
Email verification is a real mailbox check, not a guess. The agent builds and verifies the list; whether anyone is contacted, on what channel and with what message stays a human decision.
Built to order, by industry
Each of these is a productized blueprint — the stages, the approval boundaries and the integration points are already specified, which is why they are a fixed scope rather than a discovery project. None of them is deployed yet. The screenshots are our own console, and they carry that label in-frame.
Clinics
Appointment, slot confirmation, reminder, check-in, record, billing hold, follow-up.
Invoice Processing
Capture, extract, match against PO, exception queue, approval, post to ledger.
Customer Support
Triage, intent classification, draft reply, escalation boundary, human release.
HR Operations
Onboarding checklist, document collection, policy Q&A, leave and claim routing.
Recruitment Screening
Application intake, structured screening, shortlist, interview scheduling.
F&B
Reservation, order intake, stock signal, supplier reorder, shift handover.
Tuition Centres
Enquiry capture, trial booking, placement, attendance, parent updates.
Lead Nurture
Segment, sequence, respond, score, hand to a human at the buying signal.
Compliance
Control mapping, evidence collection, gap register, review cadence.
Beauty & Wellness
Booking, package tracking, rebooking prompt, therapist roster.
A sales demo you can talk to
Voice Receptionist
A multilingual booking and enquiry receptionist, running as working software on sample business data.
Real software you can talk to in a sales call. Not deployed against a live booking system.
Who you are dealing with
Entity: VYR (Arvion)
Based: Singapore
Singapore buyers check ACRA before they co-invest. Publishing this removes a step rather than adding one.
Book a scoping call
Thirty minutes. We will tell you which of the pipelines above is running and which one we would be building for you.
Social Media
Calendar, draft, brand check, schedule, engagement triage.