What is Macha — AI Copilot for Your Tools

What Macha is

Macha is an AI copilot platform for support, e-commerce, and operations teams. You connect the tools your team already uses — Zendesk, Freshdesk, Gorgias, Front, Shopify, Stripe, Slack, Confluence, Notion, Google Workspace — and Macha turns each connected tool into a set of capabilities your AI agents can use. Agents read data from your workspace, draft replies, update records, and take action on your behalf, with confirmation gates on anything customer-facing.

The model under the hood matters less than what the agent is allowed to do. Macha is built around the tool surface, not the chat box: every agent is configured with a specific set of tools (read, write, knowledge), specific instructions (the rules you write), and an optional trigger (the event that wakes it up). The model just executes the policy you've written.

The core idea

Most AI products are a chat box pointed at a generic large language model. Macha is different in two ways:

• Tools, not just chat. Every agent has a specific tool surface — search Zendesk tickets, read a Google Doc, look up a Stripe customer, post a public reply to a Front conversation. The agent decides which tool to call when, but it can only call tools you've enabled.
• Your rules in writing. Each agent has an instructions field where you write the policy in plain English: how to triage, what to escalate, what tone to use, what's off-limits. The agent obeys those instructions on every conversation.

The result is an agent that behaves like a junior teammate who knows your tools — not a chatbot that guesses.

What you can build with Macha

Most customers fall into one of four shapes:

• Support automation. A triage agent reads incoming tickets and tags / prioritizes / routes them. A reply assistant drafts customer responses. An escalation manager catches the urgent ones. Each works off your existing helpdesk (Zendesk, Freshdesk, Gorgias, Front) and uses the helpdesk's native concepts — tags, priorities, custom fields, internal notes.
• E-commerce ops. Order lookup agents resolve "where is my order?" by reading Shopify. Refund handlers verify eligibility from Stripe before processing. Product recommenders search your catalog. Macha doesn't replace your store — it gives your support team an AI that can read it.
• Internal Q&A. Knowledge agents answer team questions using uploaded docs, Confluence, Notion, or Google Drive. The "where do I find X?" question gets a direct answer with a link to the source.
• Multi-agent flows. A parent agent classifies an incoming ticket and hands off to specialist sub-agents (one for refunds, one for shipping, one for technical) — each sub-agent has its own narrower toolset. The parent handles routing; the children handle the work.

Key concepts

Five primitives compose every Macha workflow. Once you know these, the rest of the platform is configuration.

Agents

An agent is a configured AI that has a name, a handle (used to @mention it), instructions (the rules it follows), a model (which AI runs it), a set of tools, and optionally a trigger. You can create agents manually or with Sidekick — describe what you want and Sidekick builds it. See Agents.

Tools

Tools are the actions an agent can take. They come from two places: connectors (Zendesk, Shopify, etc. — pre-built tool sets) and custom tools (any HTTP API you connect via the Custom Tools builder). Each tool is either a read (safe — fetches data) or a write (mutates external state — needs confirmation in chat, runs immediately in autonomous mode).

Connectors

Connectors are the integrations Macha ships with — currently 14, including all major helpdesks (Zendesk, Freshdesk, Gorgias, Front), e-commerce (Shopify), payments (Stripe, Razorpay), messaging (Slack), docs (Confluence, Notion, Google), and a few more. Each connector shows up with its full tool surface the moment you connect it. See Connectors.

Triggers

Triggers are the events that wake an agent up without a human typing. A Zendesk "new ticket" trigger fires the agent every time a customer files something. A Slack app_mention trigger fires when someone @mentions the bot. A custom webhook trigger lets you call Macha from anywhere. Triggers are how agents go from "interactive chat" to "autonomous worker." See Triggers.

Data sources

Data sources are the knowledge an agent reads from. Upload PDFs, point at a website to crawl, or hook up live sources like Google Docs or Confluence. The agent searches across them on every conversation. Different from connectors: connectors give the agent actions, data sources give the agent context. See Data Sources.

How Macha is different from a generic chatbot

Three things, mostly:

• Macha is multi-tool by default. A single Macha agent typically has 8–20 tools active across multiple connectors. The model decides which one to call. Generic chatbots either have no tools or one at a time.
• Macha enforces your rules at the platform level. Write tools always pause for confirmation in chat. Connector-disabled tools are invisible to the agent at runtime. Plan limits apply uniformly. You don't have to trust that the LLM will read your instructions correctly — the platform ensures it can't even attempt the things you've turned off.
• Macha grades itself. The Agent Evaluation feature runs an AI judge over every past conversation and scores how well the agent followed its instructions. You don't have to guess whether a prompt change helped — measure it.

Who Macha is for

Macha is built for teams who have one or more of these problems:

• Support volume that's growing faster than headcount. Triage and reply assistants take the routine work off your queue so humans focus on the hard tickets.
• Multiple tools that don't talk to each other. Macha is the layer that does — your support agent can look up a Stripe payment, search a Confluence runbook, and post the reply in Zendesk in one workflow.
• An expanding ops team that needs internal answers fast. Sidekick + connected docs means "where do I find X?" gets a sourced answer in seconds.
• A product where the support experience matters. Macha's confirmation gates, evaluation scores, and per-agent permissions are built around the assumption that bad replies are worse than slow replies.

If your stack is "just a chat box" or "just an Excel file," Macha is probably overkill. If your stack is "Zendesk + Shopify + Confluence and we're drowning in tickets," this is exactly what we built.

How to get started

1. Create an account — free trial, no card required.
2. Walk through onboarding — pick your tools, pick up to three pre-built agents, connect your integrations.
3. Land in the welcome conversation — Macha has already pulled real items from your workspace and shows what it found. This first chat is on us (zero credits).
4. Activate the agents you picked. Test them in chat first, then add a trigger when you trust them.

For a deeper walkthrough see Getting Started. For best practices on writing instructions, picking models, and avoiding common pitfalls, the Building Effective Agents guide is the field manual.