Shared human-agent workspace coordination portal

For

Engineering and operations teams that run AI agents alongside people on shared projects

Solves

Teams and AI agents work in separate tools, so handoffs, decisions and approvals are invisible and agents act without human control.

Delivers

A permissioned workspace where humans and agents own work, review results and hand off tasks

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Give teams and AI agents one shared workspace with visible handoffs and approval gates.

1. Create one shared workspace for people and agents.
2. Add AI agents as first-class participants who own work and hand off tasks.
3. Keep conversations and decisions visible to everyone.
4. Require human approval before agents take real-world actions.
5. Connect your own LLM provider accounts or local models.
6. Choose a model per agent or set a workspace default.
7. Run recurring tasks on a schedule and notify a person only when a decision is needed.
8. Reuse successful agents and flows across retries, waits, schedules and handoffs.
9. Store agent configurations, API keys and connected accounts locally on each computer.
10. Create configurable AI specialists with dedicated instructions, memory and tools.
11. Show humans and agents as nodes on a live org chart with clickable traces.
12. Wake agents on events instead of constant compute.
13. Support MCP-compatible agents and out-of-the-box integrations.
14. Separate agent output from human conversation using threaded messages.
15. Manage projects, roles and shared context for multiple agents from one control surface.
16. Orchestrate tasks and jobs with session history, task-level execution details and approval workflows.
17. Show model selection, activity logs and token or usage tracking per task or agent.
18. Support local-first, open-source customization and self-hosting.

Everything these tools do, in one app

• Shared team workspace A single place where team members and AI agents work together on projects.Found in Offloop, Spaces, Offsite and 2 more
• AI agents as participants AI agents join the workspace as first-class participants who can own work, review results, and hand off tasks.Found in Offloop, Offsite, Inline
• Visible conversations and decisions Keeps team conversations and decisions visible to everyone in the workspace.Found in Offloop, Spaces, Offsite and 1 more
• Human approval gates Requires human approval before agents take real-world actions, showing full conversational lineage.Found in Offloop, Offsite, AgentOS
• Bring your own model Lets users connect their own LLM provider accounts or local models without the tool reselling tokens.Found in Offloop, Spaces
• Per-agent model selection Allows choosing a specific model for each agent or setting a workspace default.Found in Offloop, AgentOS
• Scheduled routines Runs recurring tasks on a schedule and notifies a person only when a decision is needed.Found in Offloop, Spaces
• Reusable agents and flows Successful agents and flows can be reused across retries, waits, schedules, and handoffs.Found in Offloop, AgentOS
• Local credential storage Stores agent configurations, API keys, and connected accounts locally on each person's computer.Found in Spaces
• Configurable AI specialists Creates AI specialists like researcher or copywriter, each with dedicated instructions, memory, and tools.Found in Spaces
• Live org-chart view Shows humans and agents as nodes on a live org chart with real-time conversation flows and clickable traces.Found in Offsite
• Event-driven agent wake Uses an event-driven wake/inbox model to avoid constant compute usage while keeping teams ready.Found in Offsite
• MCP-compatible agents Supports out-of-the-box integrations and MCP-compatible agents that can be spun up with memory and guardrails.Found in Offsite
• Thread-based messaging Separates agent output from human conversation using threaded messages.Found in Inline
• Centralized agent management Organizes projects, roles, and shared context for multiple agents from one control surface.Found in AgentOS
• Task and job orchestration Manages tasks and jobs with session history, task-level execution details, and approval workflows.Found in AgentOS
• Runtime visibility Provides model selection, activity logs, and token/usage tracking per task or agent.Found in AgentOS
• Local-first open-source Supports customization and self-hosting with a local-first, open-source architecture.Found in AgentOS

What goes in, what comes out

The workflow

1. In
   Start with

   Team conversations, agent configurations, connected accounts and task history

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect team conversations

4. 3

   Agent configurations

5. 4

   Connected accounts and task history

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A permissioned workspace where humans and agents own work, review results and hand off tasks

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workspace overview and org chart, Agent and flow configuration, Task and approval queue. Use a live org-chart view with humans and agents as nodes, a central task board with threaded conversations, and a right-hand panel for agent instructions, model choice and runtime logs. Let users compare agent versions side by side. Display draft, awaiting approval and approved states. Provide a client preview link with comments anchored to the relevant task. Make the task-specific outcome a permissioned workspace where humans and agents own work, review results and hand off tasks visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Team-owned repositories, authorized task systems and permitted communication sources. Cloud storage, identity providers and deployment destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: create one shared workspace for people and agents; add AI agents as first-class participants who own work and hand off tasks. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering and operations teams that run AI agents alongside people on shared projects use it to solve "teams and AI agents work in separate tools, so handoffs, decisions and approvals are invisible and agents act without human control"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Approved handoffs per week and unapproved agent actions.
4. Measure, then decide. Track approved handoffs per week and unapproved agent actions; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create one shared workspace for people and agents; add AI agents as first-class participants who own work and hand off tasks. Support the third module with operator review: keep conversations and decisions visible to everyone. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around a permissioned workspace where humans and agents own work, review results and hand off tasks. Retain the explicit scope boundary: One workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human.

What the build depends on. Workspace setup, agent configuration, asynchronous jobs, editable version history, reviewer access and tested export formats. High-fidelity operations require specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined workspace package. Offer a monthly production allowance after repeat demand. Quote complex integrations or self-hosted deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded permissioned workspace where humans and agents own work, review results and hand off tasks. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Give teams and AI agents one shared workspace with visible handoffs and approval gates. Demonstrate a concrete permissioned workspace where humans and agents own work, review results and hand off tasks using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and operations teams that run AI agents alongside people on shared projects professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample permissioned workspace where humans and agents own work, review results and hand off tasks from a small authorized input set, with a transparent calculation of approved handoffs per week and unapproved agent actions and no promised savings.

The first 30 days

1. Week 1: interview five engineering and operations teams that run AI agents alongside people on shared projects and inspect a recent example of teams and AI agents work in separate tools, so handoffs, decisions and approvals are invisible and agents act without human control.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure approved handoffs per week and unapproved agent actions, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Approved handoffs per week and unapproved agent actions. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Approved handoffs per week and unapproved agent actions; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a permissioned workspace where humans and agents own work, review results and hand off tasks. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved agent configurations, workspace constraints and review examples, together with reliable delivery for a narrow operational niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and operations teams that run AI agents alongside people on shared projects. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Offloop, Spaces, Offsite, Inline, AgentOS, freelancers, generic automation tools and existing project applications. Compare this product with the buyer's present method on approved handoffs per week and unapproved agent actions. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a permissioned workspace where humans and agents own work, review results and hand off tasks. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve team voice, source attribution, decision accuracy and usage permissions. Named owners approve substantive changes and external actions. One workspace with a fixed set of connected accounts and approved models; final approval and real-world actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.