Definition: A Taskade autonomous agent is an AI Agents v2 instance that runs inside your workspace: it keeps persistent memory across sessions, calls any of 34 built-in tools, wakes up on triggers, and logs every action it takes. This page covers the Taskade-native mechanics. For the general concept and the loop that powers it, start with autonomous agents. For choosing how much independence to give an agent, see autonomous vs assistive agents.
TL;DR: Taskade autonomous agents are AI Agents v2 with tools, memory, triggers, and an audit trail, running inside the same workspace that holds your projects and data. You describe the job, set the guardrails, and the agent works between your check-ins. Build one free →
Unlike open-source frameworks that ship a runtime and ask you to build everything else, Taskade ships the runtime plus memory, identity, observability, and the workspace itself. The agent is not bolted onto your work. It lives where the work already is.
What Makes a Taskade Agent Autonomous
A Taskade agent is not a prompt-and-respond chatbot. Each agent has six ingredients:
- Instructions and persona: the brief that defines what the agent is for and how it behaves
- Tools: slash commands, custom commands, public tools like
/weband/code, and workspace tools like read project, update task, or send a Slack message - Memory: workspace-scoped knowledge covering documents, projects, and prior conversations, persistent across sessions
- Triggers: events that wake the agent up, such as a webhook, a schedule, an inbox arrival, or a button click
- Audit trail: every action logged with attribution and timestamp
- Permissions: 7-tier RBAC controls who can see, edit, or invoke each agent
Put together, this is a software entity that takes initiative, remembers, and is accountable. That is the autonomous part.
The 34 Built-In Tools
Every Taskade agent draws on the same toolbox of 34 built-in tools. The headline capabilities: web search for live information, code execution for real computation, file analysis for documents and data, custom slash commands for your own repeatable operations, persistent memory, public embedding so an agent can serve people outside your team, multi-model access, and multi-agent collaboration.
Model choice is handled for you. Agents draw on 15+ frontier models from OpenAI, Anthropic, Google, and open-weight providers, with the right model picked automatically for each job, so you configure what the agent should do rather than which model to run.
What Taskade Agents Do Day to Day
The practical value shows up in four buckets of recurring work:
- Task automation: categorize and tag tasks from their content, prioritize by your business logic, estimate durations, and recommend owners based on skills and workload
- Knowledge retrieval: answer team questions from your workspace docs, surface the relevant project or document, and connect people with resources that already exist
- Meeting support: summarize discussions, capture decisions, and turn outcomes into assigned follow-up tasks
- Content work: draft, edit, brainstorm, and adapt writing to your team's voice using workspace context
Because agents act inside the workspace, an instruction is enough to trigger real changes. A few examples you can type today:
- "Create a task titled 'Website Redesign' with #important tag"
- "Add #urgent to all past due tasks and notify assigned team members"
- "Schedule all #important tasks for tomorrow at 8 AM with notifications"
- "Complete all tasks assigned to me and generate a completion summary"
Each of these becomes tool calls the agent executes, not suggestions you apply by hand.
Triggers: How Agents Wake Up
Autonomy needs a starting gun. In Taskade, agents wake on events:
| Trigger | Example |
|---|---|
| Schedule | "Every Monday at 9am, run the weekly digest agent" |
| Webhook | "When Stripe sends a payment event, run the AR agent" |
| Inbox arrival | "When a new Gmail with subject Lead: lands, run the SDR agent" |
| Calendar | "When a Calendly booking is created, run the prep agent" |
| Button click | "When a user clicks Suggest reply, run the support agent" |
| Time on field | "If this lead has been in Prospecting for 5 days, nudge the SDR agent" |
| @-mention | "When someone @-mentions me in a project, react" |
Every trigger maps to an automation, every automation can hand off to one or more agents, and automations reach across 100+ bidirectional integrations that pull events in and push data out. That closes the autonomous loop: an event arrives, an agent acts, the result lands back in your workspace.
Multi-Agent Teams
A single agent is rarely the best agent for a hard goal. Many problems split cleanly into roles, such as a researcher, a writer, and an editor, each with a narrow brief and clear hand-off rules. Taskade ships multi-agent teams as a first-class primitive: agents can call each other, share project memory, and cooperate on a single task. See also multi-agent interference for what happens when teams run in parallel instead of sequentially.
Agents Inside Taskade Genesis Apps
Agents do not have to live behind a chat box. In Taskade Genesis, you describe an app in plain English and get a live app with agents working behind its tiles: a client portal whose agent drafts follow-ups, a dashboard whose agent keeps statuses current, an intake form whose agent triages every submission. TSK-1, the Taskade System Kernel, coordinates the models, memory, agents, and workflows so the app runs as one system rather than a stack of separate tools. The launch story is in Introducing TSK-1.
Human Control and Audit
Autonomous never means unsupervised. Taskade agents pause for human approval before sensitive steps, especially actions that touch external systems, and no data is exchanged with an outside service without explicit permission. Every action the agent takes is logged with attribution and a timestamp, so you can replay exactly what happened and why. You set the goal and the guardrails. The agent does the work and asks before crossing the lines you drew.
Taskade Agents vs Open-Source Claws
There is a related concept worth knowing: AI claws, coined by Andrej Karpathy in March 2026. Claws are persistent autonomous agents you self-host (OpenClaw, NanoClaw). They give you maximum control and a maximum security burden. Taskade agents give you a managed environment: less control over the runtime, far less security and scaling burden, and a workspace built for collaboration. See AI Claws for the full comparison.
Capabilities Shipped Through v6.164
Taskade Genesis has shipped a lot of agent capability in the last year. Highlights from v6.140 → v6.164:
| Version | Capability | Who It's For |
|---|---|---|
| v6.150 | Ask-Questions tool: agent can pause and ask clarifying questions mid-task | Anyone running long-running agent workflows |
| v6.150 | Auto Top-Up: credits auto-purchase when usage crosses a threshold | Teams running agents 24/7 |
| v6.151 | App Users (beta): first-class end-user management for published Taskade Genesis apps | Teams shipping agent-powered apps to non-Taskade users |
| v6.153 | Memory Graph (/memory): visualize the Workspace DNA knowledge graph |
Anyone debugging agent context |
| v6.153–6.155 | Secure sandbox for Space Agents | Agents that run real code |
| v6.154 | Custom Bash Commands: user-defined shell scripts as agent tools | Teams with bespoke ops scripts |
| v6.160 | 15+ frontier models from OpenAI, Anthropic, Google, and open-weight providers | Teams that want broader model choice |
| v6.161 | utils.jsonExtract (JSONPath) and setProjectTitle action |
Automation authors |
| v6.162 | ask_ai_structured: typed JSON output via user JSON Schema |
Anyone wiring agent output into downstream systems |
| v6.162 | website.summarize: one-click URL-to-summary action |
Research and lead-gen workflows |
| v6.163 | Bulk-Delete UI for project listings + agent reference libraries | Teams managing large agent libraries |
| v6.163 | Enterprise BYOK: bring your own OpenAI / Anthropic key | Enterprise customers with strict data policy |
Cost Model
Free tier includes a one-time AI credit allocation; paid plans add monthly credits, and credit packs use volume-tiered pricing. See the canonical breakdown on Pricing and Credits; those pages stay in sync as plans evolve, so we don't pin specific numbers here.
For 24/7 agent workloads, Auto Top-Up (v6.150+) prevents agents from stalling mid-loop when credits run out.
Related
- Autonomous Agents (the concept)
- Autonomous vs Assistive Agents
- AI Claws (OpenClaw / Karpathy)
- Autonomous Workflows
- Multi-Agent Systems
- AI Agents Index
- Autonomous Workspace
- TSK-1 (Taskade System Kernel)
- Blog: Introducing TSK-1
- Blog: Autonomous Agent Actions, Import Anything & Generate Automations
- Blog: What Is Agentic AI?