This page is a condensed summary of the entire Abundly documentation. It covers what the platform does, how agents work, available features and integrations, security architecture, and practical guides. For full details on any topic, follow the links to the relevant documentation pages.Documentation Index
Fetch the complete documentation index at: https://docs.abundly.ai/llms.txt
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Introduction
Welcome
Abundly is an enterprise-grade platform to build, configure, manage, and collaborate with autonomous AI agents — an operating system where teams of humans and AI agents work side by side. Unlike simple tool-enabled AI chat, Abundly agents offer true autonomy (schedules, triggers, 24/7 operation), multi-modal communication (SMS, email, Slack, voice calls), conversational configuration, multi-agent collaboration, dynamic apps and scripts, and enterprise security and governance. Example use cases include cross-platform automation (“dig through last week’s Slack messages and create a Notion overview doc”), scheduled monitoring (“every morning, check my HubSpot todos and ping me on Slack if something is urgent”), and bulk data processing across spreadsheets and integrations. Full page →What is an AI agent?
An Abundly agent is a multilingual, autonomous digital colleague — faster and cheaper than humans, more intelligent and flexible than code, sitting in between. Agents are designed to augment humans by handling routine knowledge work that involves fuzzy inputs, uncertainty, and judgment, freeing people for strategic tasks. Every agent has four components: an LLM (Claude, GPT, Gemini, or others) as its brain, a Mission (natural-language instructions that are versioned and can be self-updated), Tools (grouped into capabilities like Slack, Gmail, code execution, web search, and 40+ integrations), and Autonomy (schedules, triggers, and proactive communication). Benefits over manual work include lower cost, higher speed, better control through written instructions, and higher consistency. Full page →Getting started
Sign up at app.abundly.ai for a free trial with credits — no credit card required. Create an agent from scratch or clone one from the Agent Catalogue. Configure your agent by chatting with it (it writes its own instructions from the conversation), enable the capabilities it needs, then test and refine. Full page →Platform overview
The Team page is your home base, showing all agents as cards with access to Members, Agent Management, and Usage & Limits in the sidebar. The Agent page is where you configure and interact with an individual agent — it includes Instructions, Docs, Capabilities, Activity, and Settings (covering communication, usage limits, agent-to-agent communication, user access, API endpoint, model selection, and more). The chat interface supports text, voice input, file uploads, Walk & Talk mode, fact checking, and a thinking toggle for more deliberate reasoning. Full page →Pricing
Abundly uses a credit-based pricing model: you pay for credits consumed when agents perform actions. You can create unlimited agents, while workspace member seats depend on your plan — see app.abundly.ai/prices for current plans and credit allotments. Each subscription includes a monthly credit allotment that refills at the start of every billing period and doesn’t carry over, while top-up credits stay on your account until used. You can set per-agent spending limits and track usage through the Usage Reports dashboard. Full page →Features
Agent Instructions
Instructions are your agent’s job description in natural language — always top of mind, included in every chat, trigger, and scheduled run. You can edit them directly, or chat with the agent and ask it to draft and update its own instructions; the Update Instructions capability is on by default with chat-only and edit-access guardrails. Every change is versioned automatically, so you can compare versions, see who made each change (you or the agent), approve or undo individual changes, and revert to any earlier version. Full page →Configurable Capabilities
A capability is a feature or integration the agent can use — platform features like document editing or image generation, external integrations like Slack or Google Drive, custom API capabilities, or MCP servers. Each agent starts with a basic set, and you add more as needed; tabs on the capabilities page split them into Built-in, API, and MCP. Start with the minimum: too few and the agent can’t do its job, too many and it gets confused about which to use. If the agent needs something that isn’t enabled, it surfaces a capability card on demand so you can toggle it on without leaving the conversation. Full page →Multi-channel Communication
Your agent isn’t stuck in a chat window — it can talk to you through chat, email, SMS, voice calls, Slack, Teams, and other collaboration tools, and you decide which channels are enabled. Chat is always on; email is built-in and toggleable; SMS and voice calls are in beta and require contacting support. Beyond text, agents work with images, audio (including transcription and voiceovers via ElevenLabs and OpenAI), documents, and diagrams — useful for daily briefings, audio email summaries, or analyzing photos a customer sends in. If multiple channels are enabled, guide the agent in its instructions on which to prefer (“only call me for urgent issues”). Full page →Advanced Chat
Each agent has a feature-rich chat similar to ChatGPT or Claude, but treated as just one of many possible communication channels. Each conversation is its own local context: previous messages plus the agent’s instructions, documents, and capabilities — anything you want available outside that chat must be saved to instructions or an agent document (diary entries are the exception). The chat supports file uploads, voice input, text-to-speech, edits to sent messages, model switching mid-conversation, multi-user collaboration with live-streamed responses, starred and searchable conversation history, and a context indicator that suggests starting a New chat with summary when conversations get long. Agents can read, create, and update agent documents and databases from chat, render rich responses (formatted text, diagrams, images, voiceovers), generate interactive apps on the fly, and run code when the Code Execution capability is enabled. Full page →Email & SMS
Every agent gets its own email address (e.g.myagent.myworkspace@agent.abundly.ai) — it doesn’t access your inbox unless you explicitly enable that. Toggle Send Email and Receive Email in capabilities; SMS works similarly but is hidden behind support enablement and needs a dedicated phone number for inbound. Agents handle attachments, CC/BCC, replies, forwards, and rich HTML formatting much like a human would. Guardrails let you restrict the agent to an allow-list of addresses or whole domains, then independently choose what happens for recipients inside the list (send, or ask for approval) and outside it (block, or ask for approval) — approval requests appear in chat or in Activity for triggers, where each card has a Notify agent checkbox to optionally tell the agent the outcome.
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Voice Communication
Voice features let you talk to your agent and have it respond in audio across the chat and on phone calls. The microphone button transcribes speech to text in most languages (with editable transcripts before sending), the speak button reads any message aloud, and Walk & Talk mode is a mostly hands-free chat variant for walking, driving, or lunch — preserving the agent’s full intelligence by routing through the normal text pipeline rather than a real-time voice model. Under Settings → Basic Information → Voice you pick the agent’s voice (OpenAI built-in, plus ElevenLabs premade, professional, and cloned voices when configured). Phone calls (beta) let anyone reach your agent — useful for the receptionist pattern where the call captures the request and the agent does the heavy lifting after with a “phone call ended” trigger; settings give per-call control over capabilities, custom instructions, voice and turn detection, transcription tuning, escalation transfer numbers, whitelists, and approval. Full page →Agent Documents
Each agent has its own file repository that works out of the box, with both you and the agent able to create, read, and edit documents under full version history. Supported types include Markdown and code (native), SVG (editable text rendered as graphics), PDFs, Word, PowerPoint, and spreadsheets (transcribed to text with originals preserved), images (vision-described), and audio/video (transcribed). Each document has a visibility level — Full (always in context), Summary (default — agent knows it exists, reads on demand), or Hidden — and a separate Publishing control for public links. Documents support folder organization, precise edits, exact/full-text/semantic search, Chat about this to start a focused conversation, downloads (text,.docx, originals, ZIP folders), and publishing (with read/create/update/delete permissions for databases and apps). Agent documents are stored in EU data centers in Stockholm; external documents (Google Drive, SharePoint, etc.) stay on the original provider’s servers.
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Agent Databases
Agent databases (also called data documents) are special agent documents that store records as JSON, similar to a lightweight MongoDB collection — instant to create with no setup (“create a database to track processed invoices”). Use them when you have many items of the same type (invoices, tickets, products); use a text document for prose. The agent inserts, updates, deletes, and queries records via natural language, supports direct queries (exact field matching) and semantic search powered by RAG (find feedback by meaning, not just keywords), and pairs naturally with interactive apps and scripts for dashboards, forms, and automation. Schemas are defined automatically in TypeScript and editable; you can import/export JSON and publish databases with read/create/update/delete permissions. For larger or shared data, connect external databases (PostgreSQL, MongoDB, etc.) via MCP — agent databases handle hundreds of records and include RAG, while external databases scale to millions, support joins, and serve multiple systems. Full page →Interactive Apps
Apps are agent documents containing executable code that renders as a UI — dashboards, forms, calculators, data browsers — that you can build by asking (“create a dashboard for our OKRs”). Two types are supported: React apps with Chakra UI, Recharts, and Lucide icons built in, and HTML apps for custom layouts, 3D, or maps; both run sandboxed and can read/write agent databases, load other documents, pull libraries from public CDNs, and call a curated set of agent tools (HTTP requests via API capabilities, BigQuery, Snowflake, Notion, etc.). Larger apps can be split into multi-file folders with anindex.html/index.jsx/index.tsx entry file. Apps live as chat documents (scoped to the conversation, easy to iterate) until you promote them to agent documents for reuse and publishing. Published apps share data via published databases; apps that call agent tools work in the agent chat but not on public links.
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Code Execution
When Code Execution is enabled, the agent can write and run JavaScript on the fly in the chat or as saved scripts (which are agent documents with version history). Code is faster, cheaper, and more reliable than token-by-token reasoning for large data, precise calculations, format conversions, and batch operations — agents still use intelligence to write the script, then let the script do the heavy lifting. Scripts have access to all the agent’s capabilities (web search, email, agent databases, external APIs), so you can build workflows like weekly customer enrichment or competitive monitoring. Scripts can also be linked directly to triggers — scheduled tasks, webhooks, HTTP API endpoints, and MCP tools — bypassing the LLM entirely for fast, deterministic execution; if part of the work needs reasoning, the script can hand off to the full agent by returning{ escalate: true, message: "..." }.
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Virtual Machine
The Virtual Machine capability gives your agent an ephemeral Linux VM with bash, Python, and Node.js pre-installed — ideal when in-chat code execution isn’t enough. The agent spins up a fresh VM (create_vm), runs commands (execute_bash), transfers files in and out (from chat attachments or agent documents), and destroys the VM when done; the same VM is reused across steps so disk state and installed packages persist. Use it for Office documents (.xlsx, .pptx, .docx, .pdf via openpyxl, python-pptx, python-docx, pypdf, pandoc), large CSV/JSON/Parquet processing with pandas, package installs (pip, npm, apt, headless browsers like Playwright), and longer-running multi-step jobs.
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Scheduled Tasks
Scheduled tasks let your agent work proactively by waking itself up at specific times. Recurring tasks run on any pattern (daily, weekly, monthly) and live in the agent’s instructions, so saving instructions creates or updates them automatically. One-off tasks are scheduled through chat and disappear after running. From Agent Instructions → Scheduled Tasks you can enable/disable tasks, view details, and Run now to trigger immediately for testing. Tasks can be script-linked to bypass the LLM entirely for deterministic, high-volume work, and each task can override the agent’s default model and thinking setting to balance cost and capability per job. When a task runs, the agent has access to its instructions, capabilities, the task description, and any agent documents it has access to. Full page →Context and Memory
What your agent knows at any moment determines how it responds, so context management is fundamental. Global context — instructions and agent documents (subject to visibility settings) — is always available. Local context is per-interaction: a chat conversation knows its own messages, a trigger sees the trigger details (e.g. 5 previous Slack messages in the channel). Memory across contexts comes from instructions and documents themselves: agents read and write their own, so you can store preferences, decisions, and accumulated knowledge there (“create a database to store everyone’s preferences and check them before communicating”). For accumulating facts and deduplicating recurring information, use agent databases with semantic search. The legacy Memory capability is retired for new agents but still works for agents that already have it. Full page →Web Search, Scraping, and Deep Research
Three capabilities give agents access to current information beyond their training data, all built in with no API keys required. Web Search (Perplexity-powered) is fast and good for general lookups and current news; Web Scraping (Firecrawl-powered) reads specific URLs and supports an inline extraction prompt so only the relevant content comes back, similar to delegating to a sub-agent; Deep Research runs comprehensive multi-source analysis over 10+ minutes and uses significantly more credits — best for high-value strategic work. Web Search and Web Scraping are on by default; Deep Research is opt-in. Combine them — search to find a page, scrape to read the live content for 100% accurate answers — and remember web search returns citation URLs you can verify, reducing hallucination risk. Full page →Multi-Agent Collaboration and Task Delegation
Abundly supports two patterns for agents working together. Delegation spins up a temporary sub-agent for a specific task (with chosen capabilities, optional document references, and even a different LLM) — primarily for context management: a parent agent reviewing a contract against three standards can delegate one sub-agent per standard, keeping its own context clean and supporting parallel processing. The Delegate Task capability is on by default and sub-agents can’t delegate further. Agent-to-agent communication connects persistent, fully-configured agents — a Twitter expert, a reusable ICP checker — configured per agent under Settings → Agent Communication with Discoverability (No one / Team / Everyone) for inbound and explicit outbound connections; calling agents see only the target’s name and description and can optionally share documents with read or read-write access. Be deliberate about agent-to-agent connections, since they increase cost and can give indirect access to sensitive data via Agent A. Before adding more agents, consider task documents — separate guidance documents the agent reads on demand. For external systems, agents can call out via MCP or HTTP, and be called via API access. Full page →Model Selection
Each agent picks the LLM that powers it. Leaving the model on (no preference) lets Abundly choose the best general-purpose model for you and update the default as new ones land. You can pick model aliases (e.g. “Claude Sonnet Latest”, recommended) that auto-upgrade after we verify them, or specific versions for full control. Most workspaces include Anthropic Claude (Opus, Sonnet, Haiku), OpenAI GPT (5.2 Pro, 5.2, 5 Mini, 5 Nano), Google Gemini (Pro 3, Flash 2.5, Flash Lite 2.5), and xAI Grok, with optional families like DeepSeek or self-hosted. Models can be set per agent and per context (chat, scheduled tasks, email, Slack, Teams, agent messages), with further per-task overrides on individual scheduled tasks. Many models support a thinking mode that trades extra processing time and credits for better reasoning; behavior depends on model (supports / requires / does not support thinking). Full page →Agent Personalization
Personalization makes agents feel like real teammates rather than generic tools. Profile pictures can be auto-generated from the agent’s name and description, generated from a custom prompt, uploaded, or surprised by the AI; the agent can update its own picture if you ask. Personality is shaped through instructions — even one sentence (“be brief and to-the-point”, “act like an eager intern”, “communicate like the butler Jeeves”) changes how responses feel, and you can vary tone by context (formal in email, brief in Slack). The Chat Start Message can be a fresh contextual greeting per chat or a fixed line, descriptions and tags help organize agents and feed multi-agent communication, and agents can update their own name, picture, and description on request. The philosophy: agents shouldn’t pretend to be human, but treating them like teammates with a bit of personality usually works better. Full page →Agent Evals
Agent Evals turn “working correctly” into something you can verify automatically — define test cases once, run them after any change to catch regressions, compare models side by side, and iterate with confidence. Each eval has a Test Prompt (simulating a user message or scenario), an optional Tools override (Included, Excluded, or Faked with canned responses — useful to prevent side effects, control inputs deterministically, or speed up tests), and an optional Grader (LLM-based Pass/Fail or Rating 1-10, scoped to full transcript or final response only) plus optional Validation rules that fail fast on tool-call assertions. Run individual evals or Run All, configure multiple Eval models to compare, and pick a separate Grader model; results are organized by run with list and grid views, error counts separated from fails, and credits split between test and grader usage. Sub-agent calls during evals are captured as nested transcripts, agent-to-agent recursion is capped at 3 levels, and evals can be exported/imported as JSON. Turning on the Evals capability lets the agent create, edit, run, and review its own evals — the foundation of eval-driven development. Full page →Activity Monitoring
Two built-in tools, surfaced under Activity, give you visibility into what your agent is doing autonomously. The agent diary is a high-level human-readable log of activities and reasoning — written automatically after triggers and after significant chat moments when Write diary entries is enabled. The activity log is a live technical log of every trigger the agent processes, with the trigger event, pre-filter, attack detection (prompt injection / jailbreak checks for untrusted email and SMS), and execution with all tool calls and credits per phase (when Token optimisation mode is on). Each entry has a Chat about this button for drilling in, and turning on the Activity Log capability lets the agent inspect its own (or connected agents’) trigger history and credit usage. Workspace admins configure who receives blocked-attack alerts. Full page →Usage & Limits
The Usage & Limits page gives workspace and team administrators visibility and control over credit consumption. Usage Reports show current balance, average daily use, balance history, and top-consuming agents, with CSV export across configurable date ranges, intervals, and aggregations. Agent limits set a workspace-wide default daily limit plus per-agent overrides — when an agent hits its limit it stops immediately and resets at UTC midnight, with yellow badges at 80% and red when exceeded; you can manually Reset Today to resume early. Team monthly limits (when teams are enabled) cap each team’s monthly spend and pause every agent in the team when reached, in addition to per-agent daily limits. A sidebar usage widget shows credits used today and a progress bar (turn on Token optimisation mode to enable it). Both LLM tokens (input, output, reasoning) and tool usage count toward credits, regardless of trigger source. Full page →Workspace Members
The Members page is where workspace admins invite users and assign roles. Every user has one of three workspace roles: Admin (full control over members, billing, capabilities, and all agents), Member (standard access based on agent-level settings), or Guest (limited, invitation-only access to specific agents). Invites accept multiple addresses at once with optional names and (when teams are enabled) direct assignment to teams, and pending invitations count toward your plan’s member cap. Every workspace must keep at least one Admin, members can leave a workspace they belong to (unless they’re the last admin), and you can fully delete your account from Profile settings → Danger Zone with a 30-day grace period. The page also shows pending invitations (with Renew/Revoke) and any active or pending Support access so you can review and revoke at any time. Full page →Teams
Teams are an enterprise feature (enabled on request) that partition a workspace’s agents and users into logical units — by department, project, or however fits. Each user can belong to multiple teams with different team-level roles (Admin, Member, Guest) layered on top of their workspace role, and each team can have its own capability settings that override workspace defaults (more restrictive only — workspace-disabled capabilities can’t be enabled per team). Workspace admins create and delete teams and set monthly credit limits from the Team management page; team admins manage members, capability settings, and limits for their team. Agents belong to exactly one team, or are global agents with no team — your workspace controls whether everyone, only admins, or nobody can create global agents. Global agents are visible only to workspace admins. Full page →Agent management
The Agent Management page is a centralized table for workspace and team administrators to search, filter, sort, and update agents in bulk. Columns cover the agent (with privacy icon, model, and team badges), today’s usage, last activity, last updated, credit details visibility, and a free-text business value summary; expanding a row shows tags, enabled capabilities (including admin-policy disabled state), API capabilities, MCP servers, and agent admins. Filter by status, privacy, teams, tags, model, admin, capabilities, API capabilities, MCP servers, usage range, and date ranges; bulk actions include enable, disable, move to team, set daily limit, and set model. The Agent limits tab lets you manage workspace defaults, per-agent overrides, and reset daily counters in one place. Capability availability policies are configured separately under Workspace management → Capabilities and Team management → Capabilities. Full page →Agent Sharing and Cloning
Cloning duplicates an agent inside your workspace or to another workspace you belong to — useful for variations, templates, or per-team copies. You can also Export an agent to a JSON file (optionally including documents, evals, and API access settings) and Import it as a new agent — handy for backups or moving between environments. Cloning copies name, description, tags, profile picture, instructions, capabilities, model, scheduled triggers, MCP servers (without auth), and the daily limit; it never copies chat history, tool credentials, access settings, instruction history, public sharing, or daily usage counters. The person performing the clone becomes Admin of the new agent, which gets fresh default access settings. Public sharing publishes the agent so anyone with a link can clone it to their own workspace — visitors see the name, description, creator, and (on click) the instructions, with optional document inclusion. Clones are completely independent: updates to the original don’t propagate. Full page →HTTP Requests: Connect to Any API
The HTTP capability is your agent’s swiss-army-knife for talking to any online service with a REST API — well-known APIs often work straight from training data, while less familiar ones can be learned through experimentation, documentation links, web search, or your own description. It supports all standard HTTP methods (GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS) and combines well with Web Search for discovering APIs, with code execution for processing large responses, and with delegation for one-pass heavy payloads. Two guardrails on the capability card: Allow requests to any URL (when off, the agent is locked to endpoints defined by enabled API Capabilities) and Require Approval (No / Yes for all / Yes except GET). For authenticated APIs, store credentials as Secrets in Shared Assets → Secrets and define an API Capability that injects them server-side — supporting bearer tokens, API key headers, basic auth, query parameters, custom headers, OAuth 2.0 client credentials, GCP service accounts, and signed JWTs (RS256/ES256), all scoped to specific teams or agents. The capability is hidden by default and enabled per workspace by support; ask the agent to “codify” working API calls into its instructions for fast, reliable reuse. Full page →API Access
The API Access tab in agent settings exposes your agent to external systems in four ways, all backed by the same API keys: HTTP API for scripts and backend services, MCP server for AI apps like Claude Desktop, Cursor, and n8n, Chat widget for embedding a floating AI chat bubble on your website, and Webhooks for receiving callbacks from services like Stripe, GitHub, or Trello. HTTP API and MCP authenticate with API keys (sent asX-Agent-Access-Key or Authorization: Bearer); webhooks rely on the URL itself as the secret. For each endpoint, MCP tool, or webhook integration you specify a handler — either a natural-language prompt to the agent or a script that runs deterministically without invoking the LLM — plus optional model overrides. The chat widget (gated behind a workspace flag) supports allowed-origins enforcement, capability scoping, a separate daily credit limit, system prompt override, demo response override, model override, and brand styling — and stays hidden if the agent is disabled, the origin isn’t allowed, or the limit is exhausted. All exposure modes log to the activity log and respect the agent’s normal capabilities and guardrails.
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Integrations
Integrations Overview
You give your agent access to external services like Slack, Google Drive, or GitHub by toggling capabilities on. Some capabilities ship with platform-provided credentials (e.g. Perplexity, send/receive email) and work out of the box; others require your own credentials via OAuth or an API token, with all secrets stored encrypted. If a capability isn’t enabled when you ask the agent to do something, it requests it on demand by showing a capability card in the conversation. Some capabilities can be configured to require user approval before the agent acts. When no built-in integration fits, you can extend your agent with an MCP server or the HTTP capability. Full page →MCP Servers
MCP (Model Context Protocol) is an open standard for connecting AI applications to external tools. You give your agent access to an MCP server by pasting its URL into the MCP Servers tab — the platform auto-detects whether OAuth or an API token is needed, discovers the available tools, and lets you toggle them individually. This lets you extend your agent beyond built-in integrations, tap into the community ecosystem, or expose your own internal systems (CRM, inventory, custom databases) as agent capabilities without waiting for platform updates. Only add servers from sources you trust — MCP credentials are encrypted with hybrid AES+RSA, and OAuth tokens are never exposed to the model directly. You can also go the other way and expose your Abundly agent as an MCP server so external apps like Claude Desktop, Cursor, or n8n can call it as a tool. Full page →Integration pricing
Whether an integration consumes credits depends on whose credentials are being used. With user-provided credentials (Slack, Notion, GitHub, Stripe, etc.), you’re already paying the provider directly, so Abundly doesn’t charge for usage. With platform-provided credentials (e.g. web search via Perplexity, image generation), Abundly pays the provider and converts the cost into credits charged to your account. LLM inference also consumes credits regardless of which integrations are involved. Credits are pooled across your entire workspace, so every agent and member draws from the same balance — you can track consumption under Usage and Limits and set per-agent limits to cap spend. Full page →Atlassian
Connect your agent to Atlassian to work across Jira, Confluence, and other Atlassian Cloud products — searching issues, creating pages, and managing projects. An Atlassian organization admin allows Abundly access via the Rovo MCP Server settings once, after which each user authenticates with their own account through OAuth. Use it for issue triage, sprint reporting, documentation sync between Jira epics and Confluence, and cross-platform updates with Slack. Full page →Anthropic
Anthropic provides the Claude family of language models, known for strong reasoning and safety features. Claude is available as one of the LLM “brain” options for your agent, configured under Settings → Advanced Config. See Model Selection for the full list and guidance. Full page →Autodesk
Connect to Autodesk Platform Services (APS) to upload CAD files, convert DWG drawings, translate 3D models (e.g. RVT → IFC, DWG → SVF2), extract model properties and element hierarchies, and generate standalone HTML 3D viewers. With APS app credentials alone, your agent can upload, convert, translate, and view; after connecting an Autodesk account via OAuth, it can also list ACC hubs, browse project folders, and download files from Autodesk Construction Cloud. Use it to convert drawings, extract Revit door schedules, drill into mechanical systems, or pull the latest IFC from a Coordination folder. Full page →BigQuery
Your agent can explore your Google BigQuery datasets and run read-only SELECT queries on your behalf, so you get revenue breakdowns, scheduled reports, and data-quality checks without writing SQL. It discovers schemas, executes queries (with a 30-second timeout and 1 GB billing limit per query), and returns results along with bytes processed so you can keep an eye on cost. Authentication uses a GCP service account JSON key with BigQuery Metadata Viewer, Data Viewer, and Job User roles, stored as team secrets. Full page →Cloud Storage
Your agent can list, read, write, and delete objects in any S3-compatible bucket — AWS S3, Cloudflare R2, Google Cloud Storage (HMAC), MinIO, Backblaze B2, or any provider that speaks the S3 API. Use it to archive email attachments, run scheduled CSV exports, fetch reference data like config files, clean up old objects, or move files into Slack workflows. Downloads are capped around 5 MiB and uploads around 10 MiB per object; credentials are stored as a workspace secret. Full page →ElevenLabs
ElevenLabs provides high-quality text-to-speech for your agent. It’s one of the voice providers available in the Text to Speech capability — your agent can pick a specific ElevenLabs voice per request when generating audio files like newsletter voiceovers or daily audio briefings. ElevenLabs voices (premade, professional, and cloned) also appear as options for the agent’s own voice used by the Speak button and Walk & Talk mode, alongside OpenAI voices. Full page →Firecrawl
Firecrawl powers your agent’s ability to read and extract content from web pages. The Web Scraping capability is enabled by default, letting your agent summarize articles, extract structured data like pricing from product pages, or compare content across multiple URLs. Combine it with Perplexity for deeper research workflows — Perplexity finds the URLs, Firecrawl extracts the detail. Full page →Giphy
Giphy lets your agent search and share animated GIFs. It’s bundled into the Image Generation capability alongside AI image generation. Use it to add a celebration GIF to team announcements, illustrate sentiment in weekly reports, or drop a thumbs up into a Slack post. Full page →GitHub
Connect your agent to GitHub to browse files, create and review pull requests, manage issues, and react to repository events. Authentication uses a personal access token (fine-grained tokens recommended), and you can specify watched repositories so the agent reacts to webhooks for new issues, pull requests, or pushes. Common patterns include code review assistance, generating release notes from commits, automated issue triage with labels, and dependency update PRs. Full page →Google Calendar
Connect your agent to Google Calendar to view events, schedule meetings, and block focus time. Authentication uses a personal access token tied to your Google account, and after connecting you choose which calendars the token can access — broad access or least-privilege scope. The capability is currently hidden; email support@abundly.ai to enable it. Full page →Google Drive
Connect Google Drive in one of two scopes: Google Drive (only specific files you share with the agent, plus files it creates) or Google Drive (Full Access) (all approved drives and folders, hidden by default). Both use a personal access token; with Full Access you pick which drives and folders to allow during OAuth. Your agent can read Google Docs, Sheets, Slides, PDFs, and common Office files; edit Docs and Sheets; and create new Docs and Sheets in your Drive. Use Reconnect to re-run consent if a token’s access has been revoked. Full page →Google Gemini
Google provides the Gemini family of language models and image generation. Gemini models are available as an alternative LLM under Settings → Advanced Config. Image generation uses Gemini Pro 3 (also known as NanoBanana) — toggle on the Image generation capability and select Google to generate infographics, cover images for daily news summaries, and other visuals. Full page →HubSpot
Connect your agent to HubSpot to read and update any data in your CRM — contacts, companies, deals, tickets, tasks, emails, and custom objects. The agent can call any HubSpot REST API endpoint your token has access to, so anything you can do in HubSpot, the agent can do. Authentication uses a private app access token with scopes you choose (you must be a HubSpot super admin to create private apps). Use it for contact lookups, deal stuck-in-stage monitoring, company research, follow-up task creation, and ticket status updates. Full page →Microsoft Teams
Connect your agent to Microsoft Teams so it appears as its own named bot — users @mention it in channels, group chats, or DMs to trigger the agent directly. Each Teams-enabled agent needs its own Teams app, bot identity, and client secret, set up across the Developer Portal, Azure Portal, and Teams Admin Center. Common patterns include triggering a deployment agent with@Releaser, kicking off research from @Researcher, or scheduled standup posts to a channel. The integration is currently hidden; email support@abundly.ai to enable it.
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Notion
Connect your agent to Notion to read pages, query databases, and create or update content. Authentication uses an internal integration token with the read/update/insert permissions you choose, and the agent can only access pages you explicitly share with the integration (child pages inherit access). Use it for CRM-style database updates from forwarded emails, content search, meeting notes linked from Slack, overdue task summaries, and file handoffs from Slack to Notion via agent documents. Full page →OpenAI
OpenAI provides a wide range of capabilities for your agent: GPT models as an alternative LLM, image generation via GPT, voice transcription via Whisper (mic button or audio file drop), text to speech (Speak button), and image recognition for screenshots, invoices, and charts. The OpenAI Realtime API also powers low-latency voice calls — Abundly combines it with Twilio for the Make Phone Call and Receive Phone Call capabilities (currently hidden, contact support to enable). Full page →Outlook
Connect your agent to Outlook to read, search, and draft emails in Microsoft 365. Authentication uses a personal access token tied to your Microsoft account; in many organizations a Microsoft admin must approve the Abundly Outlook app in Microsoft Entra first. By default the agent only has read and draft tools — toggle Allow sending email to let it send directly. Use it for inbox summaries, attachment search, daily digests posted to Slack, and SMS-driven email follow-ups. The integration is currently hidden; email support@abundly.ai to enable it. Full page →Perplexity
Perplexity powers your agent’s web search and deep research. It’s a platform-provided integration with no setup — Web Search (enabled by default) returns answers in seconds for quick facts and lookups, while Deep Research takes longer but produces comprehensive analysis with extensive citations. Use it for competitive analysis, daily news monitoring, due diligence on new leads, and market research with academic sources. Full page →SharePoint
Connect your agent to SharePoint to list, read, create, and edit documents in your Microsoft 365 document libraries. Authentication uses a personal access token tied to your Microsoft account, and after connecting you choose which sites and folders to allow. Text-based files (txt, md, csv) can be read and edited; for Office documents the agent can read content but not edit it directly. Use it for document search, content retrieval from handbooks, weekly summary reports, and cross-platform sync between Outlook and SharePoint folders. The integration is currently hidden; email support@abundly.ai to enable it. Full page →Slack
Your agent communicates with you and your team in Slack with its own identity — its own Slack app, bot user, and token. People @mention it or DM it, and it replies in the same thread with full context, capabilities, and history, just like in the portal chat. Two modes are available: Chat mode (default), where Slack threads behave as persistent agent chats, and Trigger mode, where each subscribed Slack event is forwarded as a raw payload for ambient monitoring or custom automations (use cautiously to avoid reply loops). Beyond posting and reacting, the agent can call any Slack Web API method — pinning messages, adding bookmarks, listing user groups — and it can both download attachments into agent documents and send files (including generated charts and SVGs) back into Slack. Setup uses an Abundly-generated app manifest with the right scopes, event subscriptions, and webhook URL pre-filled. Full page →Snowflake
Your agent can explore your Snowflake databases and run read-only queries so you get insights, reports, and reconciliation checks without writing SQL. Authentication uses a Snowflake Programmatic Access Token (PAT) — no password — with your Account Identifier and Login Name, stored as a team secret. Queries time out after 30 seconds; no DDL or DML is supported. Use it for revenue rankings, scheduled weekly signup summaries posted to Slack, cross-source data reconciliation, schema discovery, and ad-hoc campaign analysis. Full page →Steep
Your agent can discover and query your Steep metrics in plain English, using the same shared definitions (MRR, conversion, etc.) your team already uses in the semantic layer. You get aggregated data — and the generated SQL when relevant — without opening a dashboard or writing SQL. Authentication uses a Steep API key stored as a team secret. Use it for metric lookups, scheduled KPI reports emailed to leadership, cross-tool workflows that pull metrics into Notion updates, and board-prep one-pagers. Full page →Trello
Connect your agent to Trello to manage cards, respond to board events, and automate your workflow. Authentication uses an API key, token, and one or more board IDs generated through a Trello Power-Up. When you configure board IDs, the agent automatically subscribes to webhooks for card created, card moved, and card commented events — mention these triggers in your agent’s instructions to drive workflows like inbox-list triage, comment auto-replies, weekly Done-list cleanup, or creating cards from incoming emails. Full page →Twilio
Twilio powers your agent’s SMS and phone call infrastructure. Available capabilities include Send SMS, Receive SMS, Make Phone Call, and Receive Phone Call — phone calls combine Twilio with the OpenAI Realtime API for natural voice conversation. Use it for appointment reminders, SMS triage, outbound demo scheduling, and inbound support intake. You can configure a whitelist of allowed phone numbers and require approval before sending. Twilio-based capabilities are currently hidden; contact support@abundly.ai to enable them. Full page →Twitter / X
Connect your agent to Twitter/X with two separate capabilities: Twitter/X Search (search tweets, get user info, analyze followers — uses an API key from TwitterAPI.io) and Twitter/X Post (post tweets and replies — uses OAuth credentials from an X Developer app with read-and-write permissions). Use it for brand-mention monitoring with sentiment summaries to Slack, competitor tracking, content publishing with approval workflows, community engagement on product questions, and lead research from new email contacts. The X API Free tier has limited write access — Basic or Pro may be required to post. Full page →Guides
Find your use case
Not every task benefits from an agent — some are better handled by code, others by humans. The sweet spot for agents lies in tasks that are routine and time-consuming, don’t require deep expertise or creativity, involve fuzzy inputs that pure code can’t handle, and carry correctable (not mission-critical) stakes. To find candidates, map how your team spends time along two axes: frequency/time investment and value of your time. Tasks that are frequent but feel like poor use of a human’s time are your best starting points. Good examples include screening incoming emails, generating weekly summaries from multiple sources, and reviewing documents against a checklist. Start with simple use cases to build intuition for how agents think and what context they need before tackling complex workflows. Full page →Agent design
Agent design is the process of figuring out what an agent should do, how it does its work, how success is measured, and how it interacts with humans and systems. The Agent Design Canvas is a framework covering ten aspects: Purpose, Triggers, Action Plan, Interfaces, Capabilities, Impact, Input, Knowledge & State, Output, and Success. The Action Plan is the heart of the design — numbered steps showing the workflow with clear human-AI handoffs. Apply the principle of least privilege when choosing capabilities, and define success in measurable business terms. Every agent needs a clearly defined human owner responsible for its behavior, analogous to an editor-in-chief who sets guardrails and follows up when things go wrong. Prototype early with fake data or simulated integrations — a useful first prototype can often be built in 30 minutes. Keep instructions clean by limiting context to what the agent actually needs, and plan for iteration: getting an agent from “works OK” to “awesome” typically takes a few rounds of refinement. Full page →Agent optimization
Every agent consumes credits when it works, with costs varying by task complexity and LLM choice. A simple news check might use around 50 credits, while screening 300 acquisition targets could use 5,000. To reduce costs, evaluate run frequency, trim context to essentials, store reference information in agent documents (with Summary visibility) rather than instructions, simplify instruction logic, and consider switching to a cheaper model if your use case allows it. In chat, the context indicator in the action bar shifts from gray through yellow, orange, and red as context grows — start a new chat when it gets high to keep responses fast and cheap. Use the Usage & Limits dashboard to track per-agent consumption and set daily limits to prevent runaway costs. Full page →Troubleshooting
Common issues fall into a few categories: contacting support from inside the app via Help & support → Contact support (a structured email to our support inbox with your verified email as reply-to, optionally including recent pages, browser info, and a screenshot), “Overloaded” errors from LLM provider traffic spikes (wait and retry, or temporarily switch models), integration failures caused by missing OAuth scopes, expired tokens, missing Slack channel invites, misconfigured webhooks, or external rate limits, and invalid verification codes that result from requesting multiple codes — codes expire after 30 minutes and a successful sign-in invalidates remaining ones. For anything not covered, contact support@abundly.ai. Full page →Use Cases
Use Cases
Abundly publishes detailed use cases at abundly.ai/use-cases, each showing the challenge, solution, and results. Below is a categorized summary. Support & knowledge base:- HR Support Agent — Answers employee questions about policies and processes, escalates sensitive issues to HR colleagues, and keeps the knowledge base current.
- Helpdesk Agent — Handles incoming support questions with consistent answers, escalates complex issues with context, and learns from every interaction.
- Lead Generation Agent — Searches the web for companies matching your criteria, verifies relevance, and delivers contact details.
- Lead Analysis Agent — Researches incoming leads, scores them against your ideal customer profile, and alerts your team to high-potential opportunities.
- Sales Proposal Agent — Transcribes meeting notes, drafts tailored proposals, and formats everything to your company template.
- Commercial Contracts Agent — Organizes your contracts, answers questions about terms and pricing, and enables analysis across your entire contract portfolio.
- Procurement Agent — Reviews procurement documentation against standards, validates compliance, and flags contradictions.
- Target Screening Agent — Researches potential investment targets, scores them against your criteria, and surfaces the most promising opportunities.
- Security Vulnerability Agent — Monitors vulnerability disclosures, filters for relevance to your codebase, and alerts the right people.
- Product Updates Agent — Monitors your repository, translates commits into plain-language release notes, and posts them to Slack.