Agent nodes
Apply model reasoning to analysis, classification, summarization, and other judgment-heavy steps.
The Taskmesh platform
From workflow idea to governed execution.
Build with agents, tools, logic, and people. Publish stable versions. Run with the right credentials. Inspect every outcome.
Compose visually
Model the full process, not just the AI prompt.
Taskmesh turns a business process into an explicit graph where each node has a clear role, contract, and place in the run history.
Tool nodes
Invoke approved operations through configured connections and explicit input mappings.
Human tasks
Pause execution for review, collect a decision, and continue with recorded context.
Control flow
Branch, switch, merge, loop, delay, or accept the first usable result.
Safe evolution
Draft freely. Publish deliberately.
Workflow authoring and production execution are separate concerns. Publishing creates a stable version so later edits cannot silently change a running business process.
Draft validationVersioned schemasImmutable releasesScoped publishing
Draft
Published version
Designed and tested by authorized builders.
Immutable definition selected for controlled execution.
May change as the team improves the process.
Preserves the exact graph, operations, and code revision.
Validation surfaces missing mappings and configuration.
Runtime inputs are checked against a stable contract.
Execution visibility
Understand what happened at every node.
A workflow run is more than a final answer. Taskmesh preserves the path, status, structured outputs, human decisions, and failure context needed to operate responsibly.
Live run state
Follow progress through the graph and see which step is running, waiting, complete, or unsuccessful.
Node-level inspection
Review structured inputs and outputs without reducing the workflow to a single opaque response.
Human decision context
Keep the request, reviewer choice, and resulting continuation associated with the workflow run.
Stable output contracts
Use versioned result envelopes and explicit merge strategies to make downstream behavior predictable.
Secure Python tool
Use code for computation without turning it into unrestricted access.
Authors can write a focused Python function for data processing. Publishing pins an immutable revision, and execution happens in a dedicated sandbox without network, credentials, resources, or a mutable root filesystem.
Pure computation
Use JSON input with Python, NumPy, and pandas for controlled data work.
Separated permissions
Authoring, testing, viewing source, and publishing remain distinct capabilities.
Isolated runtime
No network access, credential access, resource mounting, or native fallback.
Connect Taskmesh to the systems your work depends on.
Turn published API operations and data tools into reusable workflow building blocks.