Conversational agent build and operations workspace

For

Product and platform teams building and running conversational AI agents for internal or customer use

Solves

Agent work is split across builder packages, orchestration libraries, evaluation scripts and separate hosting, so teams rent several tools and still cannot see, test or approve what agents do in production.

Delivers

A deployed, monitored agent with human approval points

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 the number of rented agent tools and keep agent definitions, conversations, traces and approvals in one owned workspace.

1. Assemble agents and workflows by dragging components on a canvas.
2. Define agents and workflows directly in code.
3. Maintain context across multi-turn conversations.
4. Connect agents to selected large language models.
5. Let agents call external services, APIs and tools.
6. Coordinate multiple agents on one workflow.
7. Define and execute agent decision-making steps.
8. Show execution traces, logs and step-level debugging.
9. Test prompts and evaluate agent performance on held-out cases.
10. Require human review and approval before consequential actions.
11. Persist agent memory and context across interactions.
12. Support real-time interaction over WebSockets.
13. Keep workflows, data tables, knowledge bases and files in one shared workspace.
14. Replace selected LLM calls with deterministic code to cut token use.
15. Run the platform on the buyer's own infrastructure.
16. Share approved agents across the organization.
17. Offer a repository of pre-built agents and components.
18. Track prompt and project changes with rollback.
19. Support real-time team collaboration on agent development.
20. Move agents from development to production with fast deployment.

Everything these tools do, in one app

• Visual agent builder Lets users assemble agents and workflows by dragging and dropping components on a canvas.Found in Sim, Agent Development Kit, Flowise
• Code-based agent building Allows developers to define agents and workflows directly in code for full control.Found in Open Agent Kit, VoltAgent, Sim
• Multi-turn conversations Enables agents to maintain context across multiple exchanges with users.Found in Open Agent Kit
• LLM integration Connects agents to various large language models for natural language understanding and generation.Found in Open Agent Kit, VoltAgent, Sim and 3 more
• External tool integration Lets agents call external services, APIs, and tools to perform actions.Found in Sim, Zen Agents (by Zencoder), Retool Agents and 1 more
• Multi-agent orchestration Coordinates multiple agents to work together on complex workflows.Found in VoltAgent
• Agent workflow management Provides tools to define, manage, and execute agent decision-making processes and workflows.Found in Open Agent Kit, Sim, Flowise
• Observability and debugging Offers visibility into agent execution steps, traces, and logs for troubleshooting and optimization.Found in VoltAgent, Flowise, LLM Spark
• Testing and evaluation Includes tools to test prompts, debug agents, and evaluate their performance.Found in Agent Development Kit, Retool Agents, LLM Spark
• Human-in-the-loop Allows humans to review and approve agent actions before they are executed.Found in Flowise
• State persistence Enables agents to maintain memory and context over time across interactions.Found in Cloudflare Agents
• Real-time communication Supports real-time interaction between agents and users via WebSockets.Found in Cloudflare Agents
• Shared workspace context Keeps workflows, data tables, knowledge bases, and files in one place so agents share memory and data.Found in Sim
• Deterministic steps Replaces some LLM calls with deterministic code to reduce token usage and cost.Found in Sim
• Self-hosting Allows the platform to be run on the user's own infrastructure.Found in Sim
• Organization-wide sharing Lets teams share custom agents across the organization to standardize practices.Found in Zen Agents (by Zencoder)
• Marketplace Provides a community-driven repository of pre-built agents and components.Found in Zen Agents (by Zencoder)
• Version control Tracks changes to prompts and projects, supporting collaboration and rollback.Found in LLM Spark
• Team collaboration Facilitates real-time collaboration among team members on agent development.Found in LLM Spark
• Instant deployment Streamlines moving agents from development to production with fast deployment.Found in LLM Spark

What goes in, what comes out

The workflow

1. In
   Start with

   Approved prompts, tool definitions, model settings, workflow steps and review rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved prompts

4. 3

   Tool definitions

5. 4

   Model settings

6. 5

   Workflow steps and review rules

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   A deployed, monitored agent with human approval points

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate agent definitions and workflow steps for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints, tool-call routing and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Model choice, tool permissions and approval rules remain buyer decisions; final release 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: Agent and workflow builder, Test and evaluation bench, Deployment and operations console. Use a project list with environment badges, a central canvas or code editor for agent definitions, and a right-hand panel for models, tools, memory and review rules. Let users compare prompt and workflow versions side by side. Display draft, in review, approved and live states. Provide a trace viewer with step-by-step execution, tool calls and human approval records. Make the task-specific outcome a deployed, monitored agent with human approval points visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, environment access, tool credentials, model allowances, evaluation cases, approval states, deployment 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 prompts, tool definitions and knowledge sources. Cloud or self-hosted model endpoints, external APIs, WebSocket channels and deployment 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

   7 days

   One buyer segment, one recurring use case; first modules: assemble agents and workflows by dragging components on a canvas; define agents and workflows directly in code. 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 product and platform teams building and running conversational AI agents for internal or customer use use it to solve "agent work is split across builder packages, orchestration libraries, evaluation scripts and separate hosting, so teams rent several tools and still cannot see, test or approve what agents do in production"?
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 production incidents per released agent.
4. Measure, then decide. Track accepted agent tasks per delivery hour and production incidents per released agent; 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 buyer team, one conversational agent and one external tool integration; final release and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: assemble agents and workflows by dragging components on a canvas; define agents and workflows directly in code. Support the remaining modules with operator review: maintain context across multi-turn conversations; connect agents to selected large language models; let agents call external services, APIs and tools; coordinate multiple agents on one workflow; define and execute agent decision-making steps; show execution traces, logs and step-level debugging; test prompts and evaluate agent performance on held-out cases; require human review and approval before consequential actions. 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 a deployed, monitored agent with human approval points. Retain the explicit scope boundary: One buyer team, one conversational agent and one external tool integration; final release and consequential actions 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 agent QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One buyer team, one conversational agent and one external tool integration; final release 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 agent package. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, monitored agent with human approval points. 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 the number of rented agent tools and keep agent definitions, conversations, traces and approvals in one owned workspace. Demonstrate a concrete deployed, monitored agent with human approval points using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams building and running conversational AI agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample deployed, monitored agent with human approval points from a small authorized input set, with a transparent calculation of accepted agent tasks per delivery hour and production incidents per released agent and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams building and running conversational AI agents and inspect a recent example of agent work split across builder packages, orchestration libraries, evaluation scripts and separate hosting.
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 production incidents per released agent, 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 production incidents per released agent. 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 production incidents per released agent; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a deployed, monitored agent with human approval points. 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 contracts, evaluation cases and review examples, together with reliable delivery for a narrow buyer team. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and platform teams building and running conversational AI agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Open Agent Kit, Scoopika - TS packages, VoltAgent, Sim, Agent Development Kit, Zen Agents (by Zencoder), Retool Agents, Cloudflare Agents, Flowise and LLM Spark. Compare this product with the buyer's present method on accepted agent tasks per delivery hour and production incidents per released agent. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, tool-call processing, 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 a deployed, monitored agent with human approval points. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve buyer data boundaries, source attribution, tool permissions and usage rights. Buyers approve agent release, tool access and consequential actions. One buyer team, one conversational agent and one external tool integration; final release 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.