AI agents are becoming much more useful when they can do more than answer a prompt.
Instead of asking an AI to explain a problem or generate a few lines of code, we can now give an agent a piece of work and let it investigate, make changes, run tests, and report back.
That changes the relationship.
The agent isn't necessarily replacing a person. It is becoming another way a team can execute work.
That is where the idea of agentic teammates comes in.
But once an agent starts behaving like a teammate, the team has to think about more than the quality of the underlying model.
How do you assign work to an agent?
How much context should you give it?
Who remains responsible when an agent performs the work?
How do you review what it did?
What happens when it gets stuck?
And how do you make sure the work doesn't disappear into someone's private AI session?
These questions are becoming just as important as the agents themselves.
What Are Agentic Teammates?
An agentic teammate is an AI agent that participates in a team's workflow by taking on defined pieces of work rather than simply responding to individual prompts.
The distinction matters.
A chatbot might help you think through a problem. An agentic teammate can potentially take the next step.
For example, a developer might create a task to investigate a failing API endpoint. An agent could inspect the relevant code, identify the problem, make a change, run tests, and return its findings.
The developer still owns the outcome.
The agent handles part of the execution.
This creates a useful division of responsibility:
People provide direction, judgment, and accountability.
Agents handle execution where they are capable of doing so.
The exact balance will depend on the task, but the important thing is that the agent becomes part of an existing workflow rather than existing as an isolated AI conversation.
Agentic Teammates Need More Than Prompts
One of the biggest differences between traditional AI assistance and agentic work is that a prompt isn't necessarily enough.
If you're asking an AI a simple question, a single prompt may contain everything it needs.
A real task is different.
An agent might need:
- the expected outcome
- relevant files or repositories
- project conventions
- technical constraints
- acceptance criteria
- available tools
- instructions about how the work should be performed
- information about what has already been tried
Without that context, the agent has to make assumptions.
And assumptions become more expensive when the agent is allowed to actually change things.
This is why agentic workflows benefit from separating persistent instructions from task-specific requirements.
The persistent instructions can explain how an agent generally works.
The task explains what needs to happen this time.
That makes the workflow easier to repeat without turning every task into a giant prompt.
Give Each Agent a Clear Role
I don't think every agent needs to be a general-purpose AI that can do everything.
In fact, specialized agents can make agentic workflows easier to manage.
Consider a software team with several recurring types of work.
One agent could focus on backend development. Another could handle testing. Another could help with documentation. Another might be configured around research or analysis.
The point isn't that each agent has to be limited to one narrow task.
The point is that the team should know what an agent is configured to do.
That makes assignment more intentional.
Instead of thinking:
Which AI should I open for this?
The question becomes:
Which capability should handle this work?
This is an important shift because it makes the agent reusable.
A team doesn't need to recreate the same instructions every time someone needs a particular type of work done.
Keep Human Ownership Clear
Giving an agent execution responsibility doesn't mean giving it ownership of the outcome.
This distinction is important.
Imagine a developer is responsible for fixing a bug. They assign an agent to investigate the problem and implement the change.
The agent might do most of the actual work.
But the developer is still the person responsible for deciding whether the result is correct and ready to move forward.
This creates two separate concepts:
Responsibility: Who owns the work?
Execution: Who or what is performing the work?
They don't have to be the same.
That separation becomes particularly important as agents become capable of working independently for longer periods.
If nobody is responsible for reviewing the result, autonomy can quickly become a liability.
How Much Autonomy Should an Agent Have?
There isn't one correct level of autonomy for every task.
A useful way to think about it is to consider the consequences of being wrong.
An agent researching possible solutions can usually operate with more freedom than an agent making a change that affects production infrastructure.
Similarly, an agent preparing documentation may need less supervision than one modifying authentication logic.
So instead of asking:
Should agents be autonomous?
I'd ask:
Which parts of this task can the agent safely handle without intervention?
That can lead to a practical division.
An agent might be allowed to:
- investigate an issue
- search a codebase
- propose an implementation
- modify files
- run tests
- prepare documentation
- summarize findings
While a person remains responsible for:
- approving important architectural decisions
- reviewing sensitive changes
- resolving ambiguous requirements
- accepting the final result
- deciding whether something is ready for production
The goal isn't maximum autonomy.
The goal is appropriate autonomy.
Put the Work Somewhere the Team Can See It
This is where agentic workflows can become difficult.
Suppose an agent spends an hour investigating a problem inside a private AI session.
It finds something important.
It makes several changes.
It encounters a blocker.
Then it eventually produces a result.
If all of that information exists only inside that person's session, what happens when someone else needs to review the work?
They have to reconstruct the conversation.
That doesn't scale particularly well.
For agentic teammates to work as part of a team, the important context should stay attached to the work itself.
The task should contain the goal and requirements.
The conversation should capture decisions and follow-up instructions.
The execution history should show what the agent actually did.
The final result should remain accessible to the people responsible for reviewing it.
This is one of the biggest differences between simply using an AI agent and actually building an agentic workflow.
How to Review Work From Agentic Teammates
An agent saying "completed" isn't the same thing as a task being complete.
The review process should focus on the actual result.
Depending on the task, that could mean checking:
- whether the original requirements were satisfied
- what files or systems were changed
- whether tests passed
- whether the implementation follows project conventions
- whether any assumptions were made
- whether the agent encountered unresolved issues
- whether anything still requires human judgment
For a coding task, this could involve reviewing the diff and test results.
For research, it could mean checking sources and conclusions.
For documentation, it might mean checking technical accuracy and completeness.
The review process should therefore be connected to the original task rather than treated as a completely separate activity.
That gives the reviewer the context they need to answer a simple question:
Did the agent actually accomplish what we asked it to do?
What Happens When an Agent Gets Stuck?
Agentic teammates will still get stuck.
The problem might be missing context.
The requirements might be ambiguous.
A test might fail.
A dependency might not be available.
The agent might reach a decision that requires someone with domain knowledge.
This doesn't necessarily mean the agent failed.
It means the workflow needs a way for humans to intervene.
A useful pattern is:
Agent works → Agent encounters blocker → Human provides context → Agent continues
The important part is that the intervention should happen within the same piece of work.
You don't want the developer to open another chat, explain the entire situation again, and manually reconstruct everything the agent already knows.
The agent should be able to receive the additional context and continue from there.
That is much closer to how collaboration with a human teammate works.
Use Comments as Part of the Workflow
This also changes how we should think about comments.
In a traditional project-management system, comments are often just discussion around a task.
With agentic teammates, they can become part of the execution loop.
A person might tell the agent:
Use the existing authentication middleware instead of introducing a new dependency.
The agent can then continue the work with that additional instruction.
Or a reviewer might say:
The tests pass, but the error handling doesn't match the existing service pattern. Please revise it.
The important thing is that the instruction stays attached to the work.
This creates a useful feedback loop without requiring the team to constantly switch between a task tracker and a separate AI conversation.
When One Agent Isn't Enough
Some tasks are simple enough for one agent.
Others naturally involve several roles.
Imagine building a new feature.
One agent might investigate the existing implementation.
Another might work on the backend.
Another might create tests.
Another might review the documentation.
You could coordinate all of these manually, but eventually the coordination itself becomes work.
This is where agent teams or groups can become useful.
A coordinating agent can interpret the overall goal and involve other agents when different capabilities are required.
The human team still remains involved, but the coordination can happen within the workflow rather than through a collection of disconnected AI sessions.
The important principle is that multiple agents should not simply mean multiple independent conversations.
There needs to be a shared context around the overall work.
Where Sharkly.AI Fits Into This Model
One practical example of this approach is Sharkly.
Sharkly treats Agents as saved working configurations rather than one-off prompts. An Agent can have instructions, Skills, a Runtime, repositories, environment settings, and run settings, and then be assigned to a Task much like a teammate. The person remains responsible while the Agent handles execution.
The interesting part is what happens around the Agent.
A Sharkly Task can hold the goal, scope, acceptance criteria, people responsible, execution assignee, comments, status, and history. A person can remain the human assignee while an Agent or Crew handles execution.
That directly reflects the separation between responsibility and execution.
It also means the work doesn't have to disappear into a private AI session.
The Agent's conversation, execution, comments, and results can remain connected to the Task. An eligible comment can continue an Agent run when the Agent is the execution assignee.
Reusable instructions with Skills
Sharkly also separates reusable practices from individual task requirements.
Skills can contain reusable instructions and supporting files and be bound to Agents that should follow the same practice, such as review rules, delivery checklists, or writing standards. One-off requirements can remain on the Task instead of being baked into the Skill.
That's a useful pattern for teams because it avoids turning every task into a completely new prompt.
The Agent knows how it generally works.
The Task explains what needs to happen now.
Using Crews for more complex work
When one Agent isn't enough, Sharkly has another layer called Crews.
A Crew consists of a leader Agent plus other Agents and People. The leader can interpret the goal and involve other Agent members when the work requires different roles or sequencing.
That fits naturally with the idea of agentic teammates.
Instead of thinking about each AI session separately, the team can think about which capability should handle each part of the work.
Chat versus tracked work
There is also an important distinction between exploration and actual team work.
Sharkly's standalone Agent Chat is intended for exploring, planning, or acting before work needs ownership. When the work needs shared status, history, or review, it can become a Task.
That distinction makes sense beyond Sharkly.
Not every conversation with an AI needs to become a tracked task.
But once an agent is doing work that other people depend on, the work needs a durable place to live.
A Practical Agentic Teammate Workflow
Putting everything together, a team could structure agentic work like this.
1. Define the outcome
Start with a clear task.
Explain what needs to be accomplished, what is in scope, what isn't, and how the result will be evaluated.
2. Assign human ownership
Make sure someone is responsible for the outcome.
The person doesn't have to perform every step themselves.
3. Choose the appropriate agent
Select an agent whose configuration matches the type of work.
A specialized agent may be more useful than a general-purpose one.
4. Give the agent the right context
Provide the repositories, files, requirements, constraints, and other information necessary for the work.
Don't assume the agent knows project-specific conventions unless they have been provided.
5. Let the agent execute
Allow it to perform the work within the boundaries you've established.
6. Keep communication attached to the task
If something changes, add the new information to the work rather than starting a disconnected conversation.
7. Review the result
Check the actual output against the original requirements.
Don't treat the agent's own completion message as approval.
8. Redirect when necessary
If the work isn't right, explain what needs to change and let the agent continue where appropriate.
9. Accept the result
The responsible human decides whether the work is ready.
This workflow creates a simple feedback loop:
Assign → Execute → Review → Redirect → Accept
The agent can handle more of the execution while the human remains connected to the important decisions.
What Good Agentic Teamwork Looks Like
The best agentic workflows probably won't look like humans disappearing and a collection of autonomous agents taking over a project.
They'll look more like teams gaining another category of teammate.
Some work will still be handled entirely by people.
Some work will be delegated to an agent.
Some work will move back and forth between people and agents.
And some larger tasks will involve multiple agents coordinated around a shared objective.
The common requirement is visibility.
Everyone should be able to understand:
What is being done?
Who owns it?
Which agent is executing it?
What context was provided?
What has happened so far?
What needs review?
What happens next?
Without those answers, agentic workflows can become difficult to manage regardless of how capable the underlying models are.
The Future of Working With Agentic Teammates
Agentic AI changes more than the tools we use.
It changes the unit of work.
For years, a task was something assigned to a person. The person opened their tools, performed the work, and reported back.
Now the execution slot can increasingly be shared between people and agents.
That means project-management systems, development workflows, and collaboration tools need to account for a new reality.
An agent needs more than a prompt.
It needs a role, context, an environment, boundaries, and a way to report its work.
A human needs more than a notification saying that an agent finished.
They need enough context to review what happened and decide what should happen next.
And teams need a shared record that keeps all of this connected.
That is why the concept of agentic teammates is more interesting than simply saying that AI can automate tasks.
The real question is not just:
What can an AI agent do?
It's:
How do we build a team workflow around what the agent can do?
Final Thoughts
Working with agentic teammates requires a slightly different mindset from using AI as an assistant.
The agent shouldn't simply receive a prompt and disappear into a private session.
It should have a defined role, enough context to perform the work, and appropriate boundaries around what it can do.
The team should remain responsible for the outcome, while the agent handles the execution it is capable of handling.
And most importantly, the work should remain reviewable.
That means keeping requirements, conversations, execution history, blockers, and results connected to the work itself.
We're still figuring out what the long-term model for human-agent collaboration will look like. But one thing is already becoming clear: the teams that benefit from agents won't just be the teams with the most capable models. They'll be the teams that learn how to organize work around them.
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