Multi-agent workflow orchestration and observability portal

For

Engineering teams building and running multi-agent AI workflows in production

Solves

Agent workflows are spread across separate orchestration, provider, memory and tracing tools, so teams cannot see or control a run end to end.

Delivers

Operator-approved workflow runs with linked evidence

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

Run and observe multi-agent workflows from one owned portal instead of stitching several rented tools.

1. Define agent workflows in code or configuration.
2. Coordinate multiple agents and compose them into larger workflows.
3. Run independent tasks and models in parallel.
4. Trigger workflows on cron schedules or intervals.
5. Track and control workflow state in real time, including signal-based control.
6. Persist agent memory across runs with pluggable backends.
7. Connect agents to tools and data through the Model Context Protocol.
8. Access multiple model providers through one consistent API.
9. Switch models with minimal code changes.
10. Route requests by priority and fall back when a provider fails.
11. Store API keys encrypted and isolated per user.
12. Run, inspect and debug workflows in a local interface.
13. Trace agent and workflow execution for diagnosis.
14. Evaluate agent outputs and workflows with built-in tools.
15. Stream inputs and outputs for real-time interaction.
16. Use pre-made RAG and human-in-the-loop building blocks.
17. Execute agents on a low-overhead async runtime.
18. Keep production agents running through partial failures.
19. Self-host and customize without vendor lock-in.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned operator-approved workflow run with source references and unresolved questions.

Everything these tools do, in one app

• Multi-agent orchestration Coordinate multiple AI agents to work together or compose them into larger workflows.Found in Compozy, Mastra, GraphBit
• Workflow definitions Define agent workflows in code or configuration so they can be run and reused.Found in Compozy, Mastra
• Parallel task execution Run multiple tasks or models at the same time to speed up processing.Found in Compozy, GraphBit
• Scheduling options Trigger workflows on a schedule using cron expressions or intervals.Found in Compozy
• Stateful workflow management Track and control workflow state in real time, including signal-based control.Found in Compozy
• Persistent agent memory Give agents a memory that persists across runs and can be plugged into different backends.Found in Compozy, Mastra
• Model Context Protocol support Connect agents to tools and data through the Model Context Protocol standard.Found in Compozy
• Unified multi-provider API Access multiple AI model providers through a single consistent API.Found in TensorBlock Forge
• Model switching Switch between different AI models with minimal code changes.Found in TensorBlock Forge
• Routing and failover Route requests based on priority and automatically fall back if a provider fails.Found in TensorBlock Forge
• Secure API key storage Store API keys encrypted and isolated per user for privacy.Found in TensorBlock Forge
• Local debugging UI Run, inspect, and debug workflows and agents in an interactive local interface.Found in Mastra
• Built-in tracing Trace agent and workflow execution to understand behavior and diagnose issues.Found in Mastra, GraphBit
• Evaluation primitives Evaluate agent outputs and workflows with built-in tools.Found in Mastra
• Streaming I/O Stream inputs and outputs for real-time interactions.Found in Mastra
• RAG and HITL primitives Use pre-made building blocks for retrieval-augmented generation and human-in-the-loop steps.Found in Mastra
• Low-overhead runtime Execute agents with low CPU overhead and predictable performance using an async, lock-free runtime.Found in GraphBit
• Crash resilience Keep production agents running reliably even when parts of the system fail.Found in GraphBit
• Open-source self-hosting Self-host and customize the platform without vendor lock-in.Found in Compozy, TensorBlock Forge, Mastra and 1 more

What goes in, what comes out

The workflow

1. In
   Start with

   Workflow definitions, provider credentials, agent memory and run traces

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect workflow definitions

4. 3

   Provider credentials

5. 4

   Agent memory and run traces

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Operator-approved workflow runs with linked 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 runtime and provider set; final production deployment and incident decisions remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workflow definition and run, Live run inspector, Delivery and handover. Use a list of workflows and runs, a large central run graph with step states, and a right-hand panel for logs, traces, memory and approvals. Let users compare runs side by side. Display draft, running, failed and approved states. Provide a shareable run report with comments anchored to the relevant step. Make the task-specific outcome operator-approved workflow runs with linked evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, workflow versions, run history, approval states, provider quotas, retention 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

Customer-owned workflow repositories, provider APIs and permitted data sources. Cloud runtime, secret storage, observability backends and deployment targets. 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

   6 days

   One buyer segment, one recurring use case; first modules: define agent workflows in code or configuration; coordinate multiple agents and compose them into larger workflows. 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 teams building and running multi-agent AI workflows in production use it to solve "agent workflows are spread across separate orchestration, provider, memory and tracing tools, so teams cannot see or control a run end to end"?
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: Successful workflow runs per engineering hour and mean time to diagnose a failed run.
4. Measure, then decide. Track successful workflow runs per engineering hour and mean time to diagnose a failed run; 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 runtime and provider set; final production deployment and incident decisions remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: define agent workflows in code or configuration; coordinate multiple agents and compose them into larger workflows. Support the third module with operator review: run independent tasks and models in parallel. 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 operator-approved workflow runs with linked evidence. Retain the explicit scope boundary: One fixed runtime and provider set; final production deployment and incident decisions remain engineering.

What the build depends on. Workflow upload and preview, asynchronous run jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed runtime and provider set; final production deployment and incident decisions remain engineering.

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 workflow package. Offer a monthly production allowance after repeat demand. Quote complex multi-tenant or regulated deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded operator-approved workflow run with linked 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

Run and observe multi-agent workflows from one owned portal instead of stitching several rented tools. Demonstrate a concrete operator-approved workflow run with linked evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams building and running multi-agent AI workflows in production professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample operator-approved workflow run with linked evidence from a small authorized input set, with a transparent calculation of successful workflow runs per engineering hour and mean time to diagnose a failed run and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams building and running multi-agent AI workflows in production and inspect a recent example of agent workflows spread across separate orchestration, provider, memory and tracing tools.
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 successful workflow runs per engineering hour and mean time to diagnose a failed run, 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: Successful workflow runs per engineering hour and mean time to diagnose a failed run. 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

Successful workflow runs per engineering hour and mean time to diagnose a failed run; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs operator-approved workflow runs with linked 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 workflow templates, provider configurations 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 building and running multi-agent AI workflows in production. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Compozy, TensorBlock Forge, Mastra, GraphBit and similar rented platforms. Compare this product with the buyer's present method on successful workflow runs per engineering hour and mean time to diagnose a failed run. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, runtime 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 operator-approved workflow runs with linked evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, credential isolation, run reproducibility and usage permissions. Engineers approve substantive changes and deployment scope. One fixed runtime and provider set; final production deployment and incident decisions remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.