Reason with agents
Use model-powered steps where interpretation and judgment add value.
Governed AI workflow orchestration
Build AI workflows your business can trust.
Design, approve, run, and monitor AI-powered workflows across APIs, business tools, and secure Python—without losing human control.
Human approvals
Versioned workflows
Auditable runs
ConnectAPIs and tools
ComposeAgents and logic
ApproveHuman decisions
ExecuteControlled actions
AuditEvery outcome
One operating layer
Bring agents, tools, and people into the same workflow.
Taskmesh gives teams a clear place to model how AI should participate in real operations—and where it must stop for a person, policy, or deterministic rule.
Act through governed tools
Connect published APIs and give each workflow only the operations it needs.
Keep people in control
Pause consequential work for review, capture the decision, and continue safely.
Compute in isolation
Run pure Python transformations in a dedicated sandbox without network or credential access.
From draft to evidence
A workflow lifecycle built for dependable operations.
Separate experimentation from production execution, then preserve the context needed to understand every run.
Design
Compose agents, tools, decisions, loops, and human tasks.
Test
Validate inputs, mappings, branches, and expected outputs.
Publish
Pin a stable, immutable version for controlled use.
Run
Execute with scoped credentials and required approvals.
Inspect
Review every step, decision, result, and failure.
Control by design
Move fast without making the workflow a black box.
Taskmesh separates authoring, publishing, execution, and approval so teams can adopt AI without surrendering operational control.
Explore security and governance- Organization-scoped access and permissions
- Explicit human approval before sensitive actions
- Immutable published workflow versions
- Credential isolation and API-only integrations
- Detailed workflow-run history and audit context
- Sandboxed Python without network access
Built for operational work
Automate the process, preserve the decision.
Use Taskmesh where AI must work with business data, policies, APIs, and accountable human review.
Reconcile records and resolve exceptions.
Gather data, identify mismatches, propose a resolution, and route material changes for approval.
FetchCompareReviewRecord
Let AI propose. Let policy decide.
Place a human task or deterministic condition between model analysis and an external action.
AnalyzeApproveExecute
Shape complex data into reliable outputs.
Extract, transform, validate, and merge API data into a stable contract for downstream work.
ExtractTransformValidatePublish
Questions
What teams need to know.
What is Taskmesh?
Taskmesh is a platform for creating, publishing, running, and monitoring AI-powered workflows. A workflow can combine agents, API tools, deterministic transformations, decisions, loops, and human approval steps.
Does every workflow have to use an AI model?
No. Taskmesh supports workflows that combine AI reasoning with deterministic tools and control-flow nodes. Teams can use a model only where it meaningfully improves the process.
Can a person approve an action before it runs?
Yes. Human-task nodes can pause a workflow, present the required context, collect an authorized decision, and preserve that decision with the run.
How does Taskmesh connect to business systems?
Taskmesh connects through published APIs and configured operations. Workflows do not need direct access to an external platform’s database.
Give AI a clear role in your operations.
See how Taskmesh turns agents, tools, approvals, and audit history into one controlled workflow.