Multi-agent canvas delivery workspace

For

Engineering teams and technical leads coordinating several AI agents on one delivery

Solves

Agent work is scattered across separate tools, so context, decisions and review state are lost between runs.

Delivers

Reviewed multi-agent canvas run linked to delivery evidence

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce coordination overhead while keeping agent work inspectable and under named human approval.

1. Create, run and coordinate multiple AI agents at once.
2. Arrange agents and tools on an unlimited 2D canvas.
3. Branch conversations in any direction without losing context.
4. Chain outputs from one agent or workflow into the next.
5. Run processing locally on the user's device with local keys.
6. Send zero telemetry or usage analytics to external servers.
7. Support macOS, Windows and Linux.
8. Provide an integrated code editor in the workspace.
9. Provide native terminal emulation beside the canvas.
10. Show local webview previews next to other tools.
11. Build agent systems with a low-code framework.
12. Add custom tools configured through YAML files.
13. Connect a wide range of large language models.
14. Hand off tasks between local and cloud mid-execution.
15. Orchestrate concurrent agents in parallel.
16. Monitor agent activity and performance.
17. Store design rules, decisions and context in an editable file and knowledge graph.
18. Connect existing AI subscriptions without platform markup.
19. Let agents critique each other's work on the same canvas.
20. Inspect, modify or self-host the source code.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned reviewed multi-agent canvas run linked to delivery evidence with source references and unresolved questions.

Everything these tools do, in one app

• Multi-agent management Allows users to create, run, and coordinate multiple AI agents simultaneously.Found in Canvas by MindPal, PraisonAI, Cursor Glass and 1 more
• Infinite canvas workspace Provides an unlimited 2D space to arrange and organize AI interactions or tools.Found in Canvas by MindPal, Catenary, Doop
• Non-linear conversation branching Enables branching conversations in any direction without losing context.Found in Canvas by MindPal
• Workflow chaining Connects outputs from one AI workflow or agent to another for sequential processing.Found in Canvas by MindPal, Catenary
• Local-first processing Runs all processing on the user's device, keeping data and keys local.Found in Catenary
• No telemetry Sends zero usage data or analytics to external servers.Found in Catenary
• Cross-platform support Works on multiple operating systems like macOS, Windows, and Linux.Found in Catenary
• Integrated code editor Includes a built-in code editor for writing and editing code within the workspace.Found in Catenary
• Terminal integration Provides native terminal emulation for command-line interactions.Found in Catenary
• Browser previews Displays local webviews for previewing web content alongside other tools.Found in Catenary
• Low-code framework Simplifies building complex AI systems with minimal coding.Found in PraisonAI
• Custom tool integration Allows adding custom tools and configuring them via files like YAML.Found in PraisonAI
• Support for many LLMs Works with a wide range of large language models (e.g., over 100).Found in PraisonAI
• Cloud handoff Switches tasks between local and cloud environments mid-execution.Found in Cursor Glass
• Parallel agent orchestration Coordinates multiple agents running concurrently to reduce context-switching.Found in Cursor Glass
• Visibility and monitoring Provides tools to monitor and track agent activities and performance.Found in Cursor Glass
• Shared memory system Stores design rules, decisions, and context in an editable file and knowledge graph.Found in Doop
• Bring-your-own-AI model Connects existing AI subscriptions (e.g., Claude, ChatGPT) without platform markup.Found in Doop
• Agent-to-agent review Enables multiple AI agents to critique each other's work on the same canvas.Found in Doop
• Open source Provides source code that can be inspected, modified, or self-hosted.Found in Doop

What goes in, what comes out

The workflow

1. In
   Start with

   Agent definitions, tool configs, model keys and project context

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent definitions

4. 3

   Tool configs

5. 4

   Model keys and project context

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed multi-agent canvas run linked to delivery evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the 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 agent topology and approved model list; final code, security and delivery decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent and tool setup, Editable canvas run, Review and delivery. Use a thumbnail gallery for projects, a large central canvas for arranging agents and branches, and a right-hand panel for context, memory and comments. Let users compare run versions side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant agent step. Make the task-specific outcome reviewed multi-agent canvas run linked to delivery evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, reviewer comments, approval states, model allowances, run limits, export 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

Customer-owned repositories, agent definitions, tool configs and permitted model providers. Cloud compute, code hosting, terminal and preview destinations. Start with file exchange and validate destination specifications before promising direct deployment. 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

   7 days

   One buyer segment, one recurring use case; first modules: create, run and coordinate multiple AI agents at once; arrange agents and tools on an unlimited 2D canvas. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 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 teams and technical leads coordinating several AI agents on one delivery use it to solve "agent work is scattered across separate tools, so context, decisions and review state are lost between runs"?
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: Accepted agent tasks per delivery hour and rework after review.
4. Measure, then decide. Track accepted agent tasks per delivery hour and rework after review; 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 agent topology and approved model list; final code, security and delivery decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create, run and coordinate multiple AI agents at once; arrange agents and tools on an unlimited 2D canvas. Support the third module with operator review: branch conversations in any direction without losing context. 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 multi-agent canvas run linked to delivery evidence. Retain the explicit scope boundary: One fixed agent topology and approved model list; final code, security and delivery decisions remain human.

What the build depends on. Agent upload and preview, asynchronous run jobs, editable version history, reviewer access and tested export formats. High-fidelity delivery requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed agent topology and approved model list; final code, security and delivery decisions 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 agent package. Offer a monthly production allowance after repeat demand. Quote complex multi-team or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed multi-agent canvas run linked to delivery evidence. 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 coordination overhead while keeping agent work inspectable and under named human approval. Demonstrate a concrete reviewed multi-agent canvas run linked to delivery evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and technical leads coordinating several AI agents on one delivery professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed multi-agent canvas run linked to delivery evidence from a small authorized input set, with a transparent calculation of accepted agent tasks per delivery hour and rework after review and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and technical leads coordinating several AI agents on one delivery and inspect a recent example of agent work scattered across separate tools, so context, decisions and review state are lost between runs.
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 accepted agent tasks per delivery hour and rework after review, 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: Accepted agent tasks per delivery hour and rework after review. 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

Accepted agent tasks per delivery hour and rework after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed multi-agent canvas run linked to delivery evidence. 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 topologies, tool configs and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams and technical leads coordinating several AI agents on one delivery. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Canvas by MindPal, Catenary, PraisonAI, Cursor Glass and Doop, plus the buyer's present mix of separate agent tools and scripts. Compare this product with the buyer's present method on accepted agent tasks per delivery hour and rework after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, local and cloud compute, 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 multi-agent canvas run linked to delivery evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, code accuracy and usage permissions. Named owners approve substantive changes and deployment scope. One fixed agent topology and approved model list; final code, security and delivery decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.