Human AI collaboration: a practical model for agentic workflows
Learn how to organize human AI collaboration with shared context, delegated authority, and clear handoffs for agentic workflows in Vaiz.
Human AI collaboration organizes work so people set goals, provide context, and review results while AI helps with analysis, drafting, and execution. Teams also need a shared record of requirements, decisions, and outcomes to continue work beyond an individual AI session.
This guide explains AI agent task delegation and coordination through shared context, clear authority, and a documented handoff.
Consider a developer who finishes a change with an AI coding agent. The code is ready for review, but the project task still contains an earlier requirement. A teammate uses that outdated task to write documentation, while the project lead asks whether development has started.
The implementation is ready, but the information needed to use it has not reached the team.
Vaiz is a work management platform where employees can work through boards, tasks, documents, or connected AI assistants using shared project context.
Start with shared project context
Shared project context includes current requirements, decisions, responsibilities, acceptance criteria, and work status. It gives people and agents a common basis for their assignments.
Imagine a software team adding CSV export to its application. The product manager defines which records users can export, a developer implements the feature, QA checks it, and a writer prepares the help article.
Link the specification to the implementation, testing, and documentation tasks. Assign someone to keep it current. Vaiz Documents keeps specifications, notes, and checklists alongside project work.
For this example, the brief should answer:
- Does the export include all records or only the filtered selection?
- Which columns appear, and in what order?
- How should dates and empty values be formatted?
- Which user permissions apply?
- What evidence will show that the feature works?
Keep unresolved questions visible and identify who will answer them.
An AI human collaboration model for team projects
This AI human collaboration model separates responsibility for decisions, project records, and execution.
Role | Responsibility | What it contributes |
|---|---|---|
Human | Sets the goal, defines authority, resolves exceptions, and accepts the result | Direction, judgment, and accountability |
System | Maintains requirements, task relationships, work status, and history | Shared context and a record of progress |
Agent | Executes the assignment within its scope and available permissions | A deliverable, verification results, and questions requiring a decision |
The diagram shows what happens when these roles are incomplete. With scattered context, a person must collect and reconcile separate agent outputs. Without a decision owner, exceptions remain unresolved. With shared records and clear ownership, people can continue coordinating work without agents.
AI agent task delegation and coordination
Task delegation defines what an agent should deliver and which decisions it may make. Coordination connects that assignment to dependencies, review, and the next person or agent responsible.
Both need to be clear before work begins.
Give the agent a specific deliverable
For the CSV export feature, “build the export” leaves important decisions open. Specify the expected behavior, link the approved requirements, and explain how the result will be checked.
Implement CSV export for records selected by the current filters. Follow the linked specification for column order, date format, and access rules.
Add tests for filtered results, empty results, and restricted access. Keep unrelated behavior unchanged.
If the specification conflicts with existing application behavior, explain the conflict before changing the requirement.
Return a summary of the changes, completed checks, checks you could not run, and remaining work.
Name the reviewer and provide the source material needed to complete the assignment.
Coordinate work between agents and people
When several agents or employees contribute, define who owns each deliverable and how their outputs connect.
For the export feature, a coding agent might prepare the implementation while an assistant helps a writer outline the documentation. The outline can use approved requirements; final instructions and screenshots depend on verified behavior.
Use these coordination rules:
- Assign a human owner to the overall outcome. This person resolves competing interpretations and confirms decisions.
- Separate deliverables. Define who owns implementation, test preparation, and documentation. Agree how overlapping edits will be combined.
- Record dependencies. Identify work that can run in parallel and work that requires an accepted result first.
- Pass on the current state. Include relevant decisions, the deliverable, verification status, and open questions.
- Resolve conflicting outputs. Record the disagreement, supporting evidence, and person responsible for the decision.
When requirements change, update the shared source and affected tasks. Confirm that active sessions receive the revision before accepting their results.
Define delegated authority for each assignment
Delegated authority defines the decisions and actions an agent may take on someone’s behalf. It specifies what the agent can do independently, what requires approval, and when to ask for clarification.
The agent may proceed | The agent should return to the owner |
|---|---|
Inspect the relevant code and specification | Requirements contradict existing behavior |
Follow established implementation patterns | The proposed approach changes access rules |
Add tests and prepare changes for review | The work requires expanding the agreed scope |
Report findings and verification results | A release or acceptance decision is needed |
Write these boundaries into the assignment and align them with the connected tools’ access and approval controls. Instructions define the intended scope; permissions determine which actions are available.
Also define what task statuses mean. An agent’s permission to update a task does not establish that its work has passed review.
A worked example in Vaiz
Consider a Vaiz task called “Add CSV export for filtered records.” It links to a specification and names the developer responsible for implementation and the product manager responsible for requirements.
The coding agent finds an existing export handler that returns every accessible record. The specification requires only records selected by the current filters.
The agent records the discrepancy and asks which behavior to preserve. The product manager confirms that the new export must follow the current filters and retain existing access restrictions. The developer records the decision and updates the acceptance criteria.
The agent prepares the change and tests. The developer reviews the implementation, QA verifies the behavior, and the writer uses the accepted result to finish the help article.
The task keeps the requirement decision connected to the implementation and its review.
Build agentic workflows around Vaiz tasks
In team projects, agentic workflows involve AI agents completing multistep assignments with context, tools, and feedback. The process also defines human review and where results are saved.
In its guide to building effective agents, Anthropic distinguishes predefined workflows from agents that choose their actions and tools dynamically. A team process can combine both: a fixed review stage can follow an agent’s investigation of a codebase.
Vaiz connects this work to shared project records.
Work through an existing AI assistant
Vaiz’s MCP connection brings workspace context into supported assistants, including Cursor, Claude, Claude Code, and Codex. Employees can use it to plan work and prepare deliverables. Available actions depend on the connection and its permissions.
Specify where the result should be saved: a task, linked document, or another agreed location. Check the saved record before handing work to a colleague.
Delegate a coding task from Vaiz
Vaiz coding sessions let teams delegate tasks to connected Cursor or Claude agents. Setup includes the repository, starting branch, model, and an option to let the agent move and update the task. Coding sessions and memory are included in the Premium plan.
Review the session report and any pull request against the acceptance criteria. The task’s Memory field keeps decisions linked to their corresponding blocks.
Review the project record
Vaiz’s Events activity history shows what changed, when, and who made the change. Use it alongside the task and deliverable to trace an unexpected update or understand how the work progressed.
Apply the model outside development
The same model can support a weekly project update.
An operations manager selects the projects and reporting period, then asks an assistant with authorized access to review the relevant tasks and documents:
Draft a project update covering completed work, current blockers, and decisions needed. Link important statements to their source tasks or documents. Flag missing or conflicting information. Leave proposed changes to dates and priorities for my review.
The assistant drafts the report and identifies a discrepancy: a task is marked complete, but a linked document still describes an unresolved dependency.
The manager checks with the task owner, corrects the source record, and approves the summary. The report is then saved where the team can access it.
The manager owns the interpretation and commitments. The assistant gathers and organizes information from the shared records.
When is an AI agent unnecessary?
An AI agent is useful when work requires interpreting context and choosing actions as new information appears. A template, fixed automation, or direct human action may be sufficient when the steps are predictable.
Moving a task when a known condition is met may fit a fixed automation. Investigating why several tasks are blocked may require an agent to inspect different sources and adjust its approach.
Start with the simplest process that meets the need, following Anthropic’s guidance on when to use agents. Add agentic steps when the improvement justifies preparation, review, and operating costs.
Make the handoff part of completion
A handoff should leave the next person ready to act. Record:
- The deliverable and its location.
- The requirement or decision it implements.
- Completed checks and their results.
- Checks that remain incomplete.
- Unresolved questions or limitations.
- The next action and its owner.
Evaluate human-AI collaboration from assignment to accepted result. Track completion time, review and correction effort, and whether colleagues can continue without reconstructing missing context.
Compare similar tasks to identify what causes delays or rework. Use those findings to improve the next brief, authority boundaries, and handoff.
Start with one task in Vaiz: connect its requirements, define the agent’s scope, and agree on the review. Refine the workflow using results your team can verify.
