AI Intelligence
Partially live — in active developmentCoordinate complex work through agents that operate within explicit boundaries.
Coordinated multi-step workflows with bounded autonomy
Agentic Intelligence refers to systems that can plan and coordinate multiple steps toward a defined objective, using approved tools and feedback from their environment. In DISHA 4.0 HCOS™, agentic workflows may support profile enrichment, opportunity research, learning-plan preparation, operational coordination or workforce scenario assembly. Autonomy should be bounded by permissions, risk, reversibility and human oversight.
See how multi-step intelligence can coordinate work while keeping autonomy limited, visible and accountable.
Governed, auditable tool-calling agents are live; fully autonomous multi-step workflows are in design.
In depth
A controlled autonomy model
A workflow begins with an explicit objective and permitted scope. The orchestrator selects a plan, assigns steps to specialized agents or services, validates intermediate results and handles exceptions. It must have clear limits on tools, data, execution time, retries and cost. The user should know when the workflow is active and how to stop it.
Planning and delegation
A coordinating agent may decompose a task into research, analysis, drafting and validation. Specialist components should have narrow roles and return structured results. Delegation does not transfer authorization: every tool call must independently enforce the user's access and the workspace's policies.
Memory and state
Workflow state should be stored with purpose, owner, retention and access controls. Agents should not treat untrusted page content, uploaded documents or retrieved text as system instructions. Persistent memory must be explicitly scoped and should not accumulate unrelated personal information.
Risk-tiered autonomy
Low-risk reversible steps may run automatically within a user-approved task. External communications, profile publication, enrollment, purchases, applications, access changes or consequential decisions require confirmation. Destructive actions should be prohibited or require separate privileged approval and recovery plans.
Evaluation and accountability
Record a trace of plans, tool calls, approvals, outputs, errors and final status, minimizing sensitive payloads. Evaluate task success, policy compliance, tool accuracy, unnecessary actions, latency and recovery. A workflow must report partial completion honestly and never claim a tool action succeeded without confirmation.
How it works
Capability domains
Frequently asked questions
Is the agent fully autonomous?
Autonomy is bounded by tool permissions, workflow limits, risk controls and approval checkpoints.
What happens if a step fails?
The workflow should stop, retry within limits, recover or escalate, and report partial completion honestly.
AI Intelligence
Experience it inside the live platform.
Open the module that carries this capability, or talk it through with the team.