Controlled agent build and run workspace

For

IT and development teams building, deploying and running AI agents inside their own business

Solves

Agent work is split across several rented tools, so building, deploying, monitoring and controlling agents means duplicated setup, scattered logs and unclear ownership of data and behavior.

Delivers

Reviewed agent build and run workspace

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce tool sprawl and keep agent behavior, data and safety controls in one owned workspace.

1. Build agents from descriptions or visual steps without code.
2. Orchestrate multiple agents with drag-and-drop or manifest design.
3. Manage the lifecycle from testing to production deployment.
4. Provide a built-in chat interface for agent interaction.
5. Monitor agent interactions, decisions and performance in real time.
6. Deploy in cloud, on-premises, hybrid or local environments.
7. Apply encryption, multi-tenancy isolation and access controls.
8. Build domain knowledge bases from uploaded data.
9. Retrieve information with semantic search.
10. Process and generate text, voice, images and video.
11. Connect agents to external tools such as Gmail, Notion, GitHub and Slack.
12. Run each agent in its own isolated container or VM.
13. Store conversation history and context as persistent memory.
14. Record detailed audit trails of agent actions and decisions.
15. Escalate to a human at configurable checkpoints.
16. Detect and address unexpected agent behavior.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed agent build and run workspace with source references and unresolved questions.

Everything these tools do, in one app

• No-code agent building Allows users to create AI agents without writing code, using visual interfaces or simple descriptions.Found in Lyzr Agent Studio, Dynamiq's Agentic AI Studio, Epsilla and 3 more
• Multi-agent orchestration Enables multiple AI agents to work together on complex tasks, often with drag-and-drop or manifest-based design.Found in Dynamiq's Agentic AI Studio, RevoClaw, CrewAI and 1 more
• Lifecycle management Supports the entire process from testing to production deployment of AI agents.Found in Lyzr Agent Studio, Dynamiq's Agentic AI Studio, Architect by Lyzr and 1 more
• Built-in chat interface Provides a ready-to-use chat UI for interacting with agents, reducing frontend development.Found in Dynamiq's Agentic AI Studio
• Observability and monitoring Offers real-time insight into agent interactions, decisions, and performance for troubleshooting.Found in Dynamiq's Agentic AI Studio, Architect by Lyzr, Arch and 1 more
• Flexible deployment options Allows deployment in cloud, on-premises, hybrid, or local environments to suit different infrastructure needs.Found in Dynamiq's Agentic AI Studio, CrewAI
• Data security and privacy Includes measures like encryption, multi-tenancy isolation, and access controls to protect data.Found in Epsilla, RevoClaw, Phrony
• Knowledge base creation Enables building domain-specific knowledge bases from uploaded data for agent use.Found in Epsilla
• Semantic search Enhances information retrieval accuracy by understanding meaning, useful for research tasks.Found in Epsilla
• Multimodal capabilities Allows agents to process and generate text, voice, images, and video.Found in Architect by Lyzr
• Tool integration Connects agents to external tools like Gmail, Notion, GitHub, and Slack without custom coding.Found in Architect by Lyzr, CrewAI
• Isolated execution Runs each agent in its own secure container or VM to prevent interference and enhance security.Found in RevoClaw, Coasty
• Persistent memory Stores conversation history and context for extended periods to improve agent recall.Found in RevoClaw
• Audit trails Records detailed logs of agent actions and decisions for review and compliance.Found in RevoClaw, Phrony
• Human-in-the-loop escalation Allows human intervention at configurable checkpoints to oversee agent operations.Found in Phrony
• Anomaly detection Identifies and addresses unexpected agent behavior to maintain reliability.Found in Phrony
• Blockchain monetization Enables creators to earn revenue from AI agents through smart contracts.Found in ALIagents.ai

What goes in, what comes out

The workflow

1. In
   Start with

   Agent definitions, approved knowledge sources, tool permissions and safety rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent definitions

4. 3

   Approved knowledge sources

5. 4

   Tool permissions and safety rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed agent build and run workspace

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured agent definitions and generate candidate agent behavior 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 deployment target and approved tool set; final safety and behavior checks remain with the responsible engineer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent build canvas, Run and monitor console, Review and audit log. Use a thumbnail gallery for agents and environments, a large central canvas for agent and multi-agent design, and a right-hand panel for knowledge sources, tools, permissions and comments. Let users compare agent versions side by side. Display draft, in review, deployed and paused states. Provide a client preview link with comments anchored to the relevant agent step. Make the task-specific outcome a reviewed agent build and run workspace visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, environment settings, client comments, approval states, usage allowances, run 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 repositories, approved knowledge sources and permitted tool accounts. Cloud or on-premises runtime, identity provider, ticketing and messaging 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: build agents from descriptions or visual steps without code; orchestrate multiple agents with drag-and-drop or manifest design. 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 IT and development teams building, deploying and running AI agents inside their own business use it to solve "agent work is split across several rented tools, so building, deploying, monitoring and controlling agents means duplicated setup, scattered logs and unclear ownership of data and behavior"?
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 runs per delivery hour and incidents after deployment.
4. Measure, then decide. Track accepted agent runs per delivery hour and incidents after deployment; 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 deployment target and approved tool set; final safety and behavior checks remain with the responsible engineer. Implement one approved input format, a bounded representative case set and the first two task modules: build agents from descriptions or visual steps without code; orchestrate multiple agents with drag-and-drop or manifest design. Support the remaining modules with operator review: manage the lifecycle from testing to production deployment; provide a built-in chat interface; monitor agent interactions; deploy in cloud, on-premises, hybrid or local environments. 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 the reviewed agent build and run workspace. Retain the explicit scope boundary: One fixed deployment target and approved tool set; final safety and behavior checks remain with the responsible engineer.

What the build depends on. Agent 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 deployment target and approved tool set; final safety and behavior checks remain with the responsible engineer.

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-agent or on-premises work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent build and run workspace. 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 agent behavior, data and safety controls in one owned workspace. Demonstrate a concrete reviewed agent build and run workspace using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development teams building, deploying and running AI agents inside their own business professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed agent build and run workspace from a small authorized input set, with a transparent calculation of accepted agent runs per delivery hour and incidents after deployment and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams building, deploying and running AI agents inside their own business and inspect a recent example of agent work split across several rented 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 accepted agent runs per delivery hour and incidents after deployment, 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 runs per delivery hour and incidents after deployment. 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 runs per delivery hour and incidents after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed agent build and run workspace. 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 patterns, tool permissions and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT and development teams building, deploying and running AI agents inside their own business. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Lyzr Agent Studio, Dynamiq's Agentic AI Studio, Epsilla, Architect by Lyzr, RevoClaw, Arch, CrewAI, ALIagents.ai, Phrony and Coasty. Compare this product with the buyer's present method on accepted agent runs per delivery hour and incidents after deployment. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, container or VM 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 the reviewed agent build and run workspace. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, tool permissions and data rights. Responsible engineers approve substantive behavior changes and deployment scope. One fixed deployment target and approved tool set; final safety and behavior checks remain with the responsible engineer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.