Shared human-agent work coordination portal

For

Engineering and operations teams that want AI agents working alongside people in shared conversations

Solves

Teams rent several separate AI chat, agent and meeting tools, so context, approvals and records are scattered across subscriptions they do not own.

Delivers

Reviewed human-agent work records with provenance

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce tool sprawl and keep human-agent work in one owned, reviewable place.

1. Run shared human-agent threads with preserved context and roles.
2. Create and customize AI agents with guided prompts, templates and shared access.
3. Coordinate multiple specialist agents under a lead agent.
4. Structure threads around specific goals and outcomes.
5. Give agents a browser and code editor to execute tasks end-to-end.
6. Make requests and receive results inside Slack.
7. Connect external apps and internal tools through MCP.
8. Add custom API calls and webhooks.
9. Switch between multiple large language models per task.
10. Pick or switch models to balance speed and quality.
11. Give agents persistent memory, personalities and scheduled responsibilities.
12. Index a knowledge base and memory of prompts, decisions and outputs.
13. Record handoffs, approvals and provenance for agent runs.
14. Run automatic security audits and connection logging.
15. Require explicit human confirmation for high-stakes actions.
16. Keep content secure, portable and extensible with access controls.
17. Define agent names, roles, owners, permissions, memory scope and app access.
18. Support self-hosted or managed gateways without exposing machines.
19. Apply rate limits, randomized delays and concurrency caps.
20. Carry stored decisions, fresh data and outputs across goals.
21. Flag rule conflicts and ask the user to decide.
22. Correct spelling, grammar and punctuation in real time with contextual suggestions.
23. Support multiple languages and dialects.
24. Adjust correction strictness to user preference.
25. Explain suggested changes.
26. Record meetings and calls for later review.
27. Capture and share audio and video notes.
28. Provide dedicated channels for team and project alignment.
29. Provide informal watercooler spaces.
30. Record screens for walkthroughs and stories.
31. Show read and playback receipts for messages, video and audio.
32. Automate summarizing discussions and generating reports.
33. Let teammates collaborate in real time in one interface.
34. Accommodate different work styles within a team.
35. Write, run, test, debug and ship code changes in a connected environment.
36. Triage tickets, move work between systems and update records.
37. Generate images alongside chat.
38. Centralize billing for the team.
39. Decompose a high-level goal into tasks and assign them to specialists.
40. Compare the reviewed result with the recorded baseline and value assumptions.
41. Capture corrections and named-owner approval before consequential use.
42. Export a versioned reviewed human-agent work records with provenance with source references and unresolved questions.

Everything these tools do, in one app

• Shared human-agent threads Humans and AI agents collaborate in the same conversation threads, preserving context and roles.Found in Glue, CoChat, Vokal and 2 more
• Custom AI agent creation Teams can create and customize AI agents with guided prompts, templates, and shared access.Found in Den, Alpaca Chat
• Multi-agent coordination Multiple specialist agents work together, often under a lead agent, to execute tasks in parallel.Found in ClawTeams
• Goal-oriented threads Conversations are structured around specific outcomes to keep discussions focused and actionable.Found in Glue
• Agent execution environment Agents have a browser and code editor to perform tasks end-to-end rather than just replying with text.Found in Faby
• Slack-native workflow Users can make requests and receive results directly within Slack.Found in Faby
• MCP-powered integrations Connect many external apps and internal tools so agents can operate across systems.Found in Glue
• API and webhook integration Expand agent capabilities by integrating custom API calls and webhooks.Found in Alpaca Chat
• Multi-LLM support Switch between different large language models to suit specific tasks.Found in Alpaca Chat
• Model selection and speed options The platform can pick or switch models to balance speed and quality for different tasks.Found in Glue
• Agent memory and personalities Agents have persistent memory, distinct personalities, and scheduled responsibilities.Found in CoChat
• Knowledge base and memory Indexed knowledge base and memory save reusable prompts, decisions, and outputs.Found in Vokal
• Event log and review workflow Records handoffs, approvals, and provenance for agent runs, enabling review and accountability.Found in Vokal
• Security audits and logging Automatic security audits and reporting for connections, with logs and approval steps for sensitive operations.Found in CoChat
• Approval gates High-stakes actions require explicit human confirmation before an agent can proceed.Found in ClawTeams
• Data portability and access controls Content is designed to be secure, portable, and extensible for teams that need to keep ownership of their data.Found in Glue
• Agent profiles and permissions Agent profiles include names, roles, owners, permissions, memory scope, and app access.Found in Vokal
• Gateway connections Support self-hosted or managed gateways to connect agents without exposing machines.Found in CoChat
• Platform-aware pacing Rate limiting, randomized delays, and concurrency caps reduce risk of triggering anti-bot detection.Found in ClawTeams
• Persistent cross-run state Stored decisions, fresh data, and outputs carry forward across goals so new tasks don't start from scratch.Found in ClawTeams
• Rule conflict detection Flags when a new rule contradicts an existing constraint and asks the user to decide.Found in ClawTeams
• Real-time spelling and grammar correction Identifies and corrects spelling, grammar, and punctuation errors in real time with contextual suggestions.Found in Respell
• Multi-language support Supports multiple languages and dialects for writing assistance.Found in Respell
• Customizable correction strictness Adjust the strictness of corrections to match user preferences.Found in Respell
• Detailed explanations for changes Provides explanations for suggested changes to help users learn.Found in Respell
• Call recording Automatically records meetings and calls for later review.Found in Stork.ai
• Voice and video notes Capture and share messages in audio or video format.Found in Stork.ai
• Dedicated channels Structured spaces for team alignment and project-related discussions.Found in Stork.ai
• Watercoolers Informal virtual meeting spots that foster spontaneous interactions.Found in Stork.ai
• Built-in screen recorder Create and share video stories or walkthroughs for clarity.Found in Stork.ai
• Read and playback receipts Indicates when messages, video, and audio conferences have been read or played back.Found in Stork.ai
• Automation of routine tasks Automates tasks such as summarizing discussions and generating reports.Found in Den
• Real-time collaboration Teammates collaborate in real time within an organized interface.Found in Den
• Flexible adoption Accommodates different work styles within a team.Found in Den
• Code-focused capabilities Write, run, test, debug, and ship code changes within a connected environment.Found in Faby
• Orchestration across tools Triages tickets, moves work between systems, and updates records as a teammate would.Found in Faby
• Image generation Generates images alongside chat functionality.Found in Alpaca Chat
• Central billing Simplifies cost management for businesses with a central billing system.Found in Alpaca Chat
• Goal decomposition An AI team lead breaks down a high-level goal into tasks and assigns them to specialists.Found in ClawTeams

What goes in, what comes out

The workflow

1. In
   Start with

   Team goals, connected tools, agent profiles and approval rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect team goals

4. 3

   Connected tools

5. 4

   Agent profiles and approval rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed human-agent work records with provenance

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 fixed team workspace and approved connector set; final approvals and consequential actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Goal and thread setup, Shared human-agent thread, Agent and gateway administration, Review and audit log. Use a thread list for goals, a large central conversation canvas showing human and agent messages with roles, and a right-hand panel for agent profiles, permissions, connected tools and approvals. Let users compare agent runs side by side. Display draft, awaiting approval, approved and blocked states. Provide a review view with comments anchored to the relevant run. Make the task-specific outcome reviewed human-agent work records with provenance visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset 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, issue trackers, chat platforms and internal tools. Cloud storage, identity providers, Slack and approved API endpoints. 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: run shared human-agent threads with preserved context and roles; create and customize AI agents with guided prompts, templates and shared access. 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

   3 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 want AI agents working alongside people in shared conversations use it to solve "teams rent several separate AI chat, agent and meeting tools, so context, approvals and records are scattered across subscriptions they do not own"?
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: Completed goals per team hour and rework after agent handoffs.
4. Measure, then decide. Track completed goals per team hour and rework after agent handoffs; 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 fixed team workspace and approved connector set; final approvals and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: run shared human-agent threads with preserved context and roles; create and customize AI agents with guided prompts, templates and shared access. Support the third module with operator review: coordinate multiple specialist agents under a lead agent. 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 reviewed human-agent work records with provenance. Retain the explicit scope boundary: One fixed team workspace and approved connector set; final approvals and consequential actions remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed team workspace and approved connector set; final approvals and consequential 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 team workspace. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist agent builds separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed human-agent work records with provenance. 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

Reduce tool sprawl and keep human-agent work in one owned, reviewable place. Demonstrate a concrete reviewed human-agent work records with provenance using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample reviewed human-agent work records with provenance from a small authorized input set, with a transparent calculation of completed goals per team hour and rework after agent handoffs and no promised savings.

The first 30 days

1. Week 1: interview five engineering and operations teams that want AI agents working alongside people in shared conversations and inspect a recent example of teams rent several separate AI chat, agent and meeting tools, so context, approvals and records are scattered across subscriptions they do not own.
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 completed goals per team hour and rework after agent handoffs, 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: Completed goals per team hour and rework after agent handoffs. 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

Completed goals per team hour and rework after agent handoffs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed human-agent work records with provenance. 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 profiles, connector configurations and review examples, together with reliable delivery for a narrow team niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering and operations teams that want AI agents working alongside people in shared conversations. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Glue, CoChat, Respell, Vokal, Den, Faby, Stork.ai, Alpaca Chat and ClawTeams, plus generic chat tools and internal scripts. Compare this product with the buyer's present method on completed goals per team hour and rework after agent handoffs. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, connector usage, 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 reviewed human-agent work records with provenance. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve team voice, source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and external actions. One fixed team workspace and approved connector set; final approvals and consequential actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.