Multi-channel agent messaging console

For

Platform and support teams connecting AI agents to team messaging channels

Solves

Agents are locked to one framework and one channel, lose context between rooms, and act without review.

Delivers

Reviewed agent presence across channels

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce integration work and unapproved agent actions while keeping one conversation history per channel.

1. Connect an agent to Slack, Teams, Discord, WhatsApp, Telegram and email.
2. Keep one shared conversation history per channel.
3. Support agents built on different frameworks and providers.
4. Run the console on the team's own infrastructure.
5. Isolate memory per channel or room.
6. Require human approval before sensitive actions.
7. Render buttons, forms and charts inside chat messages.
8. Persist agent memory across sessions.
9. Launch agents without managing servers.
10. Connect agents to channels without writing code.
11. Route messages through one unified messaging API.
12. Map a person's channel accounts to one identity.
13. Show a full trail of messages, tool calls and delivery status.
14. Run scheduled tasks such as a timed summary post.
15. Answer technical questions with repo-specific code examples and diagrams.
16. Restore an agent to its last saved state after a crash.
17. Build a shared team memory base agents can query at runtime.
18. Hand a conversation to a person when needed.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed agent presence across channels with source references and unresolved questions.

Everything these tools do, in one app

• Multi-channel agent presence Lets an AI agent participate in conversations across messaging platforms like Slack, Teams, Discord, WhatsApp, Telegram, and email.Found in Switch, CopilotKit Channels SDK, AgentSky and 4 more
• Shared conversation context Keeps a single conversation history and context so the agent can follow along with what people have said in the channel.Found in Switch, AgentSky, Novu Connect and 2 more
• Framework-agnostic agent support Works with agents built on different frameworks and providers instead of locking you into one stack.Found in Switch, CopilotKit Channels SDK, AgentSky and 2 more
• Self-hosting option Lets teams run the software on their own infrastructure for control over data and deployment.Found in Switch, CopilotKit Channels SDK, Novu Connect and 1 more
• Per-channel memory Gives each channel or room its own separate memory and context so information doesn't leak between them.Found in Switch, Yasmine Works, Oasis
• Action approval controls Requires explicit human approval before the agent takes sensitive actions like sending email or updating records.Found in CopilotKit Channels SDK, Yasmine Works, Keiki
• Interactive UI in chat Renders buttons, forms, charts, and other UI elements inside chat messages instead of plain text.Found in CopilotKit Channels SDK, Novu Connect
• Persistent agent memory Remembers past conversations and decisions so the agent stays context-aware across sessions.Found in MaxClaw by MiniMax, Yasmine Works, Oasis and 1 more
• One-click agent deployment Launches and runs agents without setting up servers or managing infrastructure yourself.Found in AgentSky, MaxClaw by MiniMax, Oasis and 1 more
• No-code setup Connects agents to channels through a simple interface without writing code.Found in PlugBear
• Unified messaging API Routes agent messages through a single interface that handles delivery, threading, and channel formatting.Found in Novu Connect
• Identity resolution Maps a person's different channel accounts to one identity so context carries across platforms.Found in Novu Connect
• Conversation inspection Shows a full trail of messages, tool calls, and delivery status so teams can review what the agent did.Found in Novu Connect, Keiki
• Scheduled tasks Runs recurring jobs like posting a summary at a set time without manual prompting.Found in Yasmine Works
• Cited technical answers Answers technical questions in chat with repo-specific code examples and architecture diagrams.Found in Clarm the AI DevRel Engineer
• Crash recovery Restores an agent to its last saved state after a crash so long tasks don't restart from scratch.Found in AgentSky
• Shared team memory base Builds an organization-wide memory from conversations and artifacts that agents can query at runtime.Found in Oasis
• Human handoff Lets the agent pass a conversation to a person when needed.Found in Keiki

What goes in, what comes out

The workflow

1. In
   Start with

   Agent endpoints, channel credentials, per-channel memory rules and approval policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent endpoints

4. 3

   Channel credentials

5. 4

   Per-channel memory rules and approval policies

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed agent presence across channels

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 channel set and approved agent frameworks; final approval and identity checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Channel and agent setup, Conversation inspector, Approval queue. Use a channel list for connected workspaces, a central thread view showing messages, tool calls and delivery status, and a right-hand panel for memory scope, identity mapping and approval rules. Let users compare agent runs side by side. Display draft, pending approval, approved and failed states. Provide a client preview link with comments anchored to the relevant message. Make the task-specific outcome reviewed agent presence across channels visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, channel versions, client comments, approval states, usage allowances, message limits, delivery 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

Team-owned agent endpoints, authorized channel credentials and permitted messaging platforms. 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

   5 days

   One buyer segment, one recurring use case; first modules: connect an agent to Slack, Teams, Discord, WhatsApp, Telegram and email; keep one shared conversation history per channel. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   10 days

   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 platform and support teams connecting AI agents to team messaging channels use it to solve "agents are locked to one framework and one channel, lose context between rooms, and act without review"?
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: Channels connected per integration hour and unapproved agent actions per month.
4. Measure, then decide. Track channels connected per integration hour and unapproved agent actions per month; 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 channel set and approved agent frameworks; final approval and identity checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: connect an agent to Slack, Teams, Discord, WhatsApp, Telegram and email; keep one shared conversation history per channel. Support the third module with operator review: support agents built on different frameworks and providers. 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 agent presence across channels. Retain the explicit scope boundary: One fixed channel set and approved agent frameworks; final approval and identity checks remain human.

What the build depends on. Agent upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist IT QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed channel set and approved agent frameworks; final approval and identity checks 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-tenant or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent presence across channels. 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 integration work and unapproved agent actions while keeping one conversation history per channel. Demonstrate a concrete reviewed agent presence across channels using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Platform and support teams connecting AI agents to team messaging channels 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 presence across channels from a small authorized input set, with a transparent calculation of channels connected per integration hour and unapproved agent actions per month and no promised savings.

The first 30 days

1. Week 1: interview five platform and support teams connecting AI agents to team messaging channels and inspect a recent example of agents locked to one framework and one channel, losing context between rooms, and acting without review.
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 channels connected per integration hour and unapproved agent actions per month, 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: Channels connected per integration hour and unapproved agent actions per month. 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

Channels connected per integration hour and unapproved agent actions per month; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed agent presence across channels. 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 channel configurations, approval policies and review examples, together with reliable delivery for a narrow IT niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for platform and support teams connecting AI agents to team messaging channels. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Switch, CopilotKit Channels SDK, AgentSky, MaxClaw by MiniMax, PlugBear, Yasmine Works, Novu Connect, Clarm the AI DevRel Engineer, Oasis and Keiki. Compare this product with the buyer's present method on channels connected per integration hour and unapproved agent actions per month. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, channel API usage, 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 agent presence across channels. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve channel permissions, source attribution, message accuracy and usage permissions. Platform owners approve substantive changes and deployment scope. One fixed channel set and approved agent frameworks; final approval and identity checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.