Singulariti · System Map
A frontier harness gives one person one smart session. Singulariti gives a whole company a team of agents that share memory, route across every model plan you already pay for, check their own work before a client sees it, and get better from what they did last week.
Architecture
Every box has a way in and a way out. All four agents sit on one bus, so each one recalls from and writes back to the same brain, sends drafts through the same judgment check, hands jobs to the same dispatch, and calls models through the same router. Judgment scores flow into the learning loop as rewards, the loop feeds doctrine and skills back into the brain, and only the owned model is fine-tuned.
The loop in motion
A client asks in Slack.
Orbit picks it up in the thread it came from, with the house voice and that client's rules already loaded.
Memory comes first.
Before the model sees the message, Orbit pulls what the brain knows about this person and this account: past asks, preferences, corrections they have made.
Intent is read.
The judgment layer tags what the person actually wants, so a quick question does not turn into a project and a project does not get a one-line answer.
Work goes to the right hands.
Orbit answers directly or posts a job to a specialist or a worker lane. Workers run on whichever seat has capacity; if every plan is spent, the job is held and resumes on its own when one frees up.
The draft is checked.
Before anything is sent, the judgment layer audits it against the brief, confirms every link resolves, and allows one revision if it falls short.
Delivery and memory.
The reply lands in the original thread. The turn, and any conclusion about the person (for example "Sean wants calls proposed, not requested"), is written back to the brain.
That night the system learns.
Finished sessions are reviewed, lessons become doctrine and skills every agent shares, and the owned model is tuned on its own data.
The workspace
Orbit is the front desk in Slack. Umbra is the workspace behind it: a full agent environment running on the company's own model, where a client or strategist turns a goal into finished, shipped growth work. Every action has a permission tier, so the agent moves fast on research and drafts and stops for a person before anything goes out or spends money.
Permission tiers
| Permission tier | What the agent may do | Examples |
|---|---|---|
| Read | Look, pull, analyze. No approval needed. | Finance, CRM and social reporting |
| Propose | Prepare the action completely, then wait for a person to approve it. | Ad launches and budget changes, client emails, publishing, browser form submits, model updates |
| Execute | Act inside a tight allowlist, with a cap and a log. | Platform QA fixes, background agent jobs |
Intake
Everything the business already produces feeds the brain on a schedule. Orbit and Umbra both load it before they answer and write back after. Client graphs are walled off from each other, and the owner's private notes never surface in a client channel.
Learning
A frontier model is trained by its vendor, on the vendor's idea of a good answer, months before it meets your clients. Nobody tells it later that the proposal went out, the client said "great job," or the campaign paid back. Singulariti treats those moments as the reward. Every finished job is scored against what actually happened, and only proven wins become training data for the company's own model.
same signals, nightly
Two speeds. Every night, the system rewrites its doctrine, skills and memory from the day's outcomes. The model itself is retrained when enough judged wins have accumulated, and a new version ships only after it beats the current one on a fixed evaluation suite and a person approves it.
Reward signals
| Signal | Why it matters | Status |
|---|---|---|
| Judge-verified completion | The judgment model confirms the objective was met and backed by proof, not just claimed. | Live |
| Delivered in the thread | Work that never reached the requester counts against the agent. | Live |
| Ship checks pass | Deploy ready, tests green, render QA passed. | Live |
| Thumbs up or down | A direct rating from the person who asked. | Captured |
| Job completion receipts | The agency job closed on time with every deliverable attached. | Captured |
| Approved first time | A proposal a person approved without edits is a strong win; a rejection is a lesson. | Next |
| Proposal sent after completion | The work was good enough to put in front of a buyer. | Next |
| Client praise or a follow-on ask | "Great job" or "do the next one" in the client's own thread or inbox. | Next |
| Revision requested | A correction is a negative reward and becomes a rule in the people model. | Next |
| Revenue and performance | Payment received, ROAS or pipeline moved after the work shipped. | Next |
Live scores work today and gates training. Captured is recorded on every job and is being cleared for training. Next is designed and on the build plan.
Coverage
The brain, the permission tiers and the reward ledger are shared. What changes by function is what the agents do and what counts as a win.
| Function | Agents do | Counts as a win |
|---|---|---|
| Paid Social Buyer | Draft campaigns, activate, shift budget within caps | Approved launch, ROAS up after the change |
| Media Planner | Build flighting and channel mix from past results | Plan approved, pacing on target |
| Media Trading | Watch delivery, flag anomalies, propose fixes | Anomaly caught before spend was lost |
| Script & Social | Scripts, reels, carousels, posting calendar | Approved first time, engagement above baseline |
| Creative | Concepts, images, video, rendered variants | Render QA pass, picked for launch |
| Copywriting | Ads, landing pages, emails in the house voice | No revision requested, conversion lift |
| Sequences drafted and sent as the owner | Replies and revenue from the send | |
| Lifecycle | Map journeys, trigger win-back and onboarding | Reactivated customers, lower churn |
| Retention | Spot churn risk from payments and support | Saved accounts, renewal paid |
| SEO | Research gaps, publish pages to the live domain | Pages indexed, ranking and traffic gains |
| PR | Angles, pitches, media lists from meeting notes | Pitch sent, coverage landed |
| Community | Read comments and creators, draft replies | Response time, sentiment up |
| Affiliate Manager | Recruit and report on partners and offer codes | New partners live, attributed sales |
| Merchandising | Find top and slow sellers, propose offers | Offer approved, sell-through up |
| Brand Strategist | Positioning and briefs from calls and research | Brief approved, client says it lands |
| Analyst | Reports, attribution, research jobs | Decision made from the report |
The honest comparison
Frontier harness here means one vendor's agent product used directly: Claude Code, ChatGPT or Codex, Copilot, or a team building on a single vendor's agents SDK.
A fabric has more moving parts than one product. The trade is more operating work for memory, choice and cost control that compound over time.
| Dimension | Frontier harness | Singulariti |
|---|---|---|
| Who it serves | One person in one session at a time. | A whole team and its clients, in the channels they already use, with agents that hand work to each other. |
| Memory | Per user or per project, and it stays inside that vendor's product. | One brain shared by every agent and model. What a client told Orbit on Monday is known to the creative agent on Thursday. |
| Model choice | That vendor's models. Switching means re-platforming. | Anthropic, OpenAI, a gateway plan and an owned model behind one router. Swap a model by editing a route. |
| Cost | Per-seat plus metered API usage that grows with every agent you add. | Flat-rate plans used first and rotated by remaining quota; metered spend is last and capped. |
| Private work | Data goes to the vendor under their terms. | Sensitive or unusual work can stay on an open-weight model you own, on GPUs you control. |
| Quality before clients see it | The model grades its own homework, if at all. | A separate judgment model checks intent, audits drafts and links, and logs every decision. |
| Getting better | Improves when the vendor ships a new model. | Improves every night from your own work, and the improvements belong to you. |
| Learning from outcomes | None. The model never hears whether the work won the client. | Proposals sent, approvals, client praise and revenue are scored as rewards and fed back nightly. |
| Agencies with many clients | Shared workspace, or a new account per client. | A walled agent per client with its own knowledge base, run from one place. |
Who gets the most from it