AgentSpace is a unified runtime where your agents live, collaborate, and execute real-world tasks across the applications and websites you use every day. Think of it as a shared operating system for autonomous agents: you log in once, grant your fleet the accesses it needs, and every agent gets its own isolated virtual machine in which it works — signing into the apps and services you use, navigating real interfaces, filling forms, uploading files, clicking buttons — and returns to you either with a finished result or with a request for approval.
Unlike chat-based assistants that require your attention, AgentSpace agents work autonomously, 24/7. Close your laptop — they keep running. Create several specialized bots for different jobs — research, operations, customer support, sales — and they work in parallel, coordinate with each other, share findings through a common memory layer, and learn from every successful pattern. This is not a smarter chatbot. It is a workforce.
What AgentSpace Is — and What It Is Not
Most “AI products” are conversation interfaces: you type, it answers, the session ends. The work still lands on you. AgentSpace inverts the model: you delegate an outcome, and the agent does the work — in real applications, over real accounts, across real time — and comes back when the outcome is achieved or a decision is needed.
| Chat assistants | Copilots | AgentSpace | |
|---|---|---|---|
| Interaction | You prompt, it answers | You prompt, it suggests | You delegate, it executes |
| Where work happens | In the chat window | In your editor | In your real apps & websites |
| Runtime | Session-bound | Session-bound | 24/7, even with your laptop off |
| Scope | One conversation | One user | A coordinated fleet of agents |
| Memory | Per conversation | Limited | Persistent, shared, semantic |
| Learning | None | None | Demonstration learning + pattern extraction |
| Trust model | You verify everything | You verify everything | Approval gates + full trace replay |
How It Works
1. Single Sign-On Agent Fleet
You authenticate once. AgentSpace’s credential vault then grants each agent exactly the access its role requires — and nothing more.
- Centralized credential management — OAuth flows, API keys, and session cookies stored in an encrypted vault, never in agent context
- Per-agent permission scopes — the support agent sees the helpdesk and the CRM read-only; the finance agent sees invoicing but not payroll; least privilege by default
- Automatic credential rotation — sessions refresh without re-authentication; expired tokens never break a long-running task
- Security auditing — every access grant, every scope change, every credential use is logged and reviewable
- Instant revocation — disable one agent’s access without touching the rest of the fleet
The result: onboarding a new agent takes minutes, and the blast radius of any single agent is bounded by design.
2. Isolated Virtual Machines per Agent
Every agent runs in its own sandboxed virtual machine with full computer-use capabilities. The agent doesn’t call a private API that doesn’t exist — it uses the software the way you do.
- Real application control — agents navigate web apps and desktop software, sign in, fill forms, upload files, click through workflows
- Isolation — one VM per agent; no cross-agent data leakage; a compromised or confused agent cannot touch its neighbors
- Snapshots & rollbacks — every session can be snapshotted before risky operations and rolled back cleanly
- Resource quotas — CPU, memory, network, and spend limits per agent prevent runaway costs
- Parallel bots — spin up several agents for different jobs and let them run side by side, each in its own machine
This is the division that makes autonomy safe: the agent gets a full computer to work with, and that computer is disposable, bounded, and observable.
3. Autonomous 24/7 Operation
AgentSpace runs in the cloud, not on your laptop. Tasks that take days keep going while you sleep.
- Long-running tasks — agents work for hours, days, or weeks with automatic checkpointing; an interrupted run resumes from its last checkpoint, not from zero
- Routines & schedules — cron-like triggers: “every morning at 8, compile the pipeline report”; “every Friday, audit open invoices”; “every hour, check the monitoring board”
- Background execution — close the laptop; the fleet keeps working and reports back when there is something to report
- Cost tracking & budgets — per-agent, per-task, and per-month spend ledgers with hard caps and soft alerts
- Self-pacing — agents decide when to act, when to wait, and when to ask, like a coworker rather than a script
4. Agent-to-Agent Coordination
A fleet is more than parallel bots. AgentSpace agents delegate to each other, share findings, and resolve conflicts the way a team does.
- Automatic task delegation — the research agent hands structured findings to the report agent; the ops agent opens tickets that the support agent communicates to customers
- Shared memory layer — a common, access-controlled knowledge space where agents publish discoveries, decisions, and artifacts
- Conflict resolution — when two agents touch overlapping work, the coordination bus detects the collision and sequences or merges the work deterministically
- Fan-out / fan-in patterns — split a large job across many agents, then synthesize the partial results into one deliverable
- Consilium reviews for high-stakes decisions — before a sensitive action, multiple agents review the plan through independent lenses (correctness, security, cost, reputation) and surface disagreements for a human decision
5. Demonstration Learning (“Watch Me”)
The fastest way to teach an agent is to show it. Record yourself performing a process once — the clicks, the decisions, the exceptions — and the agent captures it as a replayable skill.
- One demonstration, permanent capability — the workflow is stored as a structured skill, not a fragile macro
- Decision points, not just clicks — the agent records why you branched (“if the invoice is over $10k, route to director approval”), not only what you clicked
- Automatic skill extraction — demonstrated workflows are distilled into named, inspectable skills and published to SkillForge for the whole fleet
- Improvement with every execution — each replay refines the skill; exceptions encountered in the wild are folded back into the procedure
You teach it once. It does it forever.
6. Human-in-the-Loop Approval Gates
Autonomy is a dial, not a switch. Every agent operates at a trust level you set — per agent, per action type, per threshold.
- Escalation by design — the agent returns to you either with a finished result or with a request for confirmation; it never silently guesses on sensitive matters
- Notifications where you live — Slack, Teams, email, and mobile push, with the exact context needed to decide in seconds
- Configurable trust levels — “the support agent may refund up to $50 on its own; above that, ask”; “the ops agent may restart services but never delete data”
- Full audit trail — every approval, every override, every autonomous decision is logged with its reasoning and its evidence
- Trust that grows — as an agent’s track record accumulates, you can widen its autonomy band — or tighten it after a miss
7. Persistent Memory & Context
AgentSpace agents don’t wake up blank every morning. They remember.
- Long-term memory across sessions — preferences, decisions, project history, and past outcomes persist across tasks and days
- Project knowledge bases — documents, wikis, and codebases indexed per project, with semantic search
- Cross-agent knowledge sharing — with access controls: the sales agent’s discovery about a client’s procurement process becomes available to the support agent handling that client
- Semantic search over accumulated context — “what did we decide about the Q3 pricing change?” returns the decision, its rationale, and its owner
8. Visual Workflow Studio
For when you want to see and shape the whole machine.
- Drag-and-drop pipeline builder — compose multi-agent workflows from skills, approvals, and integrations without code
- Real-time monitoring — watch the fleet live: who is working on what, in which app, at which step
- Trace viewer — every screen, every click, every decision, replayable frame by frame; when something looks wrong, you see exactly what the agent saw
- One-click session replay — rewind any past run for debugging, auditing, or training new agents on real examples
The Delegation Contract
Everything in AgentSpace follows one contract between you and your fleet:
An agent returns either with a finished, verified result — or with a request for confirmation. It never returns “probably done.”
That contract is enforced mechanically:
- Outcome verification — where a task has an objective check (a report generated, a ticket closed, a reconciliation balanced), the agent must produce the evidence, not a claim
- Code work is verified by Reckon — any software change an agent makes passes exit gates, disk-reconciled accounting, and structural scanners before it reaches you
- Uncertainty escalates — when evidence is undecidable, the agent asks; a guess is never shipped as a result
- NOT DONE is a valid return value — an agent that could not finish says so clearly, with what it tried and what blocked it, so you can re-delegate intelligently
A Day in AgentSpace
- 07:45 — The research agent delivers the overnight digest: 12 sources synthesized, 3 anomalies flagged, full trace attached.
- 09:10 — The support agent has resolved 41 tickets autonomously; 3 escalations wait in your approval queue, each with context and a recommended action.
- 11:00 — The ops agent triages a monitoring alert, restarts a degraded service, and files the incident report — within its trust band, no approval needed.
- 13:30 — The sales agent researches 18 target accounts, drafts personalized outreach for the 6 that match your criteria, and queues them for your one-click review.
- 17:00 — The finance agent reconciles the day’s invoices; one discrepancy over threshold is escalated with the exact documents involved.
- 23:00 — You close the laptop. The fleet keeps working: the research agent starts tomorrow’s deep-dive; the ops agent runs its hourly checks.
- 03:00 — A routine sweeps stale data, updates the knowledge base, and publishes two new skill candidates to SkillForge from patterns it proved overnight.
You didn’t prompt once. You approved four times.
Real-World Workflows
Case 1: Research & Intelligence Agent
Challenge. A strategy team spent two days per week pulling data from 12 sources, synthesizing findings, and drafting reports.
AgentSpace approach. One login granted the agent read access to the source apps. The agent runs overnight: pulls, cross-checks, synthesizes, drafts. A “Watch Me” session taught it the team’s report format and anomaly criteria.
Results. Research cycle cut from 3 days to 4 hours of agent time; the morning digest arrives before the team does; 300+ sources processed per run; every claim traceable to its source screen.
Case 2: Customer Support Agent
Challenge. A support team drowning in tickets, with slow responses after hours.
AgentSpace approach. The agent works the helpdesk 24/7 in its own VM, resolving standard issues end-to-end in the real tools. Refunds above $50 escalate with full context. Resolutions are verified by the ticket state, not by the agent’s summary.
Results. 78% of tickets resolved autonomously; first-response time under 2 minutes at 3 a.m.; every escalation shows exactly why it escalated; customer CSAT up 19%.
Case 3: Operations & Monitoring Agent
Challenge. Alert fatigue: engineers woken by noise, real incidents buried.
AgentSpace approach. The ops agent watches dashboards, triages alerts against runbooks stored in the shared memory, remediates within its trust band (restarts, cache clears, scale-ups), and escalates the rest with evidence.
Results. 91% of alerts handled without a human; mean time to remediate down 64%; engineers on call only for genuine incidents.
Case 4: Sales Prospecting Agent
Challenge. Reps spending mornings on research instead of selling.
AgentSpace approach. The agent researches target accounts across the web and the CRM, scores them against your criteria, drafts personalized outreach, and books the drafts into a one-click review queue. Coordination with the CRM agent keeps pipeline data current.
Results. Reps start the day with a reviewed shortlist; outreach volume ×4 with personalization intact; meeting rate up 31%.
Case 5: Back-Office & Finance Agent
Challenge. Manual invoice processing with error-prone reconciliation.
AgentSpace approach. A demonstrated workflow (“Watch Me”) taught the invoice pipeline once. The agent processes invoices in the real accounting software, reconciles against bank data, and escalates discrepancies over threshold.
Results. 96% of invoices processed touch-free; discrepancies caught same-day; month-end close shortened by 5 business days.
Security & Trust Model
- Least-privilege scopes — every agent’s access is a named, auditable grant, revocable instantly
- Credential vault & secret hygiene — secrets never enter agent context; subprocess environments are stripped; outputs are redacted before re-entering the model
- Isolation — one VM per agent; symlink-aware workspace containment; no lateral movement between agents
- Outbound-action guard — anything irreversible or visible to others (publish, deploy, send, transfer) requires an explicit operator grant or an approval gate
- Snapshots & rollbacks — risky operations run against a restorable state
- Complete audit & replay — every screen, click, decision, and approval, stored and replayable
- Budget caps — hard spend limits per agent and per task; autonomy without open-ended liability
Integration with the Artena Stack
Reckon → Verified code work. When an AgentSpace agent touches software, Reckon’s honest gates decide “done”: exit gates, disk-reconciled accounting, reward-hack scanners. A green claim without proof never ships.
SkillForge → Skills in, skills out. Agents consume certified skills from SkillForge on every task; skills proven in AgentSpace — including everything taught through “Watch Me” — flow back into the registry with audit trails and ablation scores.
Dream Team → Certified agents only. Agents deployed to AgentSpace are Dream Team graduates: certified in simulation, continuously recertified by production performance. Production experience flows back into training scenarios.
Core Principles
Stop prompting. Start delegating. The unit of work is an outcome, not a message. You describe the result and the guardrails; the agent finds the way.
Autonomy is a dial. Trust levels per agent and per action. Widen the band as evidence accumulates; tighten it after a miss.
Return with a result or a question — never with a guess. The delegation contract. Undecidable evidence escalates; it never masquerades as done.
Agents are coworkers. Treat them like new hires: onboard them, show them the process once, give them bounded authority, review their judgment calls, and let proven track record earn autonomy.
Memory compounds. Every task leaves the fleet smarter. Shared knowledge, demonstrated skills, and extracted patterns turn individual wins into fleet-wide capability.
Verify, don’t trust. Screens recorded, traces stored, gates enforced. Autonomy you can audit is autonomy you can afford.
Technical Architecture
- Fleet Orchestrator — agent lifecycle, scheduling, routines, delegation, conflict resolution
- Identity & Credential Vault — SSO ingestion, scoped grants, rotation, revocation, secret hygiene
- VM Provisioning Layer — per-agent sandboxed machines, snapshots, quotas, rollback
- Computer-Use Runtime — browser and desktop control, form filling, uploads, visual grounding
- Memory & Knowledge Layer — long-term memory, project knowledge bases, semantic search, cross-agent sharing
- Coordination Bus — delegation, fan-out/fan-in, consilium reviews, collision detection
- Approval Engine — trust levels, thresholds, notification routing, audit trail
- Trace & Replay Store — screen-level recording, decision logs, one-click replay
- Workflow Studio — visual pipeline builder over skills, approvals, and integrations
Getting Started
- Connect — sign in once; grant the vault the accounts your fleet will use.
- Create your first agent — pick a role (research, support, ops, sales, finance) and a permission scope.
- Show it once — record a “Watch Me” session for your core workflow.
- Set the trust dial — what it may do alone, what it must ask.
- Let it run — watch the trace viewer for the first week; widen autonomy as evidence accumulates.