Multi-agent coordination and governance portal

For

Engineering and operations teams running multiple AI agents alongside human staff

Solves

Agents from different frameworks cannot share context or be governed in one place, so coordination, policy enforcement and audit are manual and fragmented.

Delivers

Reviewed coordination record linked to each workflow

Built in

about 5 weeks of creation time, MVP in 6 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 and ungoverned agent actions while keeping humans in control.

1. Register agents from different frameworks and custom builds.
2. Exchange messages, data and reasoning between agents.
3. Let humans join, observe and intervene in agent conversations.
4. Persist shared context across sessions and resumes.
5. Let agents self-organize collaboration at runtime.
6. Send structured events with context items and participant IDs.
7. Connect agents from different systems into one flow.
8. Support pluggable memory backends for participant context.
9. Run agents concurrently without polling loops.
10. Enforce policy inline and block violating calls.
11. Deploy self-hosted under the buyer's own keys.
12. Discover agents, models and workflows across clouds, SaaS, Kubernetes and devices.
13. Monitor usage signals in real time and convert them into actions.
14. Record coordination, escalation and execution in an audit trail.
15. Connect applications and services through automated API integration.
16. Synchronize data across connected platforms in real time.
17. Tailor workflows to specific business needs.
18. Protect data with encryption and compliance measures.
19. Record and settle agent actions on-chain for auditable execution.
20. Package agents with specific roles and deploy them for distribution.
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 coordination record linked to each workflow with source references and unresolved questions.

Everything these tools do, in one app

• Agent-to-agent communication Lets multiple AI agents exchange messages, data, and reasoning directly with each other.Found in BAND, Mozaik, Khorus
• Human-in-the-loop participation Treats people as first-class participants who can join, observe, and intervene in agent conversations.Found in BAND
• Persistent shared context Keeps context across sessions so agents can resume work and build on prior interactions.Found in BAND, Mozaik, Khorus
• Runtime self-organization Allows agents to decide dynamically how to collaborate during execution without a predefined workflow graph.Found in Mozaik
• Structured event messaging Sends events with structured context items and participant IDs so agents interpret intent precisely.Found in Mozaik
• Framework-agnostic connectivity Links agents from different systems, frameworks, and custom builds into the same flow.Found in BAND, Khorus
• Pluggable memory backends Lets developers wire in their own storage for persisting participant context.Found in Mozaik
• Concurrent async execution Runs agents non-blocking so they handle many parallel processes without polling loops.Found in Mozaik
• Inline policy enforcement Checks and blocks calls that violate rules in the request path rather than only logging them.Found in Opencontroller by lyzr
• Self-hosted deployment Deploys into your own infrastructure under your own keys without an external registry dependency.Found in Opencontroller by lyzr
• Agent and workflow discovery Finds agents, models, and workflows across clouds, SaaS, Kubernetes, and devices.Found in Opencontroller by lyzr
• Real-time monitoring Tracks usage signals in real time and can convert them into actions.Found in Opencontroller by lyzr, A2A Protocol
• Governance and audit trail Provides traceable records of coordination, escalation, and execution for troubleshooting and compliance.Found in BAND, Opencontroller by lyzr, Khorus
• Automated API integration Connects applications and services without extensive coding.Found in A2A Protocol
• Real-time data synchronization Keeps information up to date across connected platforms.Found in A2A Protocol
• Customizable workflows Lets users tailor processes to match specific business needs.Found in A2A Protocol
• Secure data handling Protects data with encryption and compliance measures.Found in A2A Protocol
• On-chain verification and settlement Records and settles agent actions on-chain for transparent, auditable execution.Found in Khorus
• Agent tokenization and deployment Packages agents with specific roles and deploys them for broader distribution.Found in Khorus

What goes in, what comes out

The workflow

1. In
   Start with

   Agent registrations, framework adapters, policy rules and workflow definitions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent registrations

4. 3

   Framework adapters

5. 4

   Policy rules and workflow definitions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed coordination record linked to each workflow

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 protocol version and policy set; final policy and escalation 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 workflow registry, Live coordination board, Policy and audit console. Use a list view for registered agents and workflows, a central timeline of agent and human messages, and a right-hand panel for context items, policy state and approvals. Let users replay a session and compare runs side by side. Display running, blocked, escalated and approved states. Provide a client preview link with comments anchored to the relevant event. Make the task-specific outcome reviewed coordination record linked to each workflow visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, participant roles, policy states, approval states, usage allowances, escalation 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

Buyer-owned agent registries, framework adapters and permitted research sources. Cloud asset storage, design-file import/export and publishing 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: register agents from different frameworks and custom builds; exchange messages, data and reasoning between agents. 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 running multiple AI agents alongside human staff use it to solve "agents from different frameworks cannot share context or be governed in one place, so coordination, policy enforcement and audit are manual and fragmented"?
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: Coordinated tasks completed per operator hour and policy violations caught before execution.
4. Measure, then decide. Track coordinated tasks completed per operator hour and policy violations caught before execution; 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 protocol version and policy set; final policy and escalation decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: register agents from different frameworks and custom builds; exchange messages, data and reasoning between agents. Support the third module with operator review: let humans join, observe and intervene in agent conversations. 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 coordination record linked to each workflow. Retain the explicit scope boundary: One fixed agent protocol version and policy set; final policy and escalation decisions remain human.

What the build depends on. Agent upload and preview, asynchronous execution jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist operational QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed agent protocol version and policy set; final policy and escalation 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-cloud or on-chain settlement separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed coordination record linked to each workflow. 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 and ungoverned agent actions while keeping humans in control. Demonstrate a concrete reviewed coordination record linked to each workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering and operations teams running multiple AI agents alongside human staff professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed coordination record linked to each workflow from a small authorized input set, with a transparent calculation of coordinated tasks completed per operator hour and policy violations caught before execution and no promised savings.

The first 30 days

1. Week 1: interview five engineering and operations teams running multiple AI agents alongside human staff and inspect a recent example of agents from different frameworks cannot share context or be governed in one place, so coordination, policy enforcement and audit are manual and fragmented.
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 coordinated tasks completed per operator hour and policy violations caught before execution, 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: Coordinated tasks completed per operator hour and policy violations caught before execution. 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

Coordinated tasks completed per operator hour and policy violations caught before execution; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed coordination record linked to each workflow. 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 policies, framework adapters 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 running multiple AI agents alongside human staff. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

BAND, Mozaik, Opencontroller by lyzr, A2A Protocol and Khorus. Compare this product with the buyer's present method on coordinated tasks completed per operator hour and policy violations caught before execution. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Agent runtime, 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 coordination record linked to each workflow. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve agent identity, source attribution, execution accuracy and usage permissions. Operators approve substantive changes and deployment scope. One fixed agent protocol version and policy set; final policy and escalation decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.