How to Build a Shared Agent That Every Teammate Can Use

Steve Kom — Thu, 29 Oct 2026 15:15:01 +0000

When a team starts using AI agents, one problem often appears quickly: everyone creates their own configuration.

One developer uses detailed instructions, another forgets important checks, and a third builds a slightly different workflow. Over time, maintaining consistency becomes harder than creating the agents themselves.

The solution is to treat an agent as a reusable team resource rather than a personal prompt.

Step 1: Define one responsibility

Start with a specific task. For example, create an agent that reviews small code changes and identifies missing tests.

Avoid vague instructions such as:

Help me with software development.

Prefer a more precise definition:

Review the assigned code changes, identify potential defects, suggest relevant tests, and summarize the findings without modifying unrelated files.

A narrow responsibility makes the agent easier to test and maintain.

Step 2: Write reusable instructions

Separate permanent instructions from task-specific requirements.

Your shared configuration should define:

The agent's role and boundaries.

The information it must inspect before acting.

The checks it should perform.

The expected output format.

The conditions that require human intervention.

For example:

Role: Code Review Agent

Before reviewing:

Read the task description and acceptance criteria.
Inspect the relevant code changes.
Identify potential defects and missing tests.

Output:

Findings and severity
Relevant files or code locations
Recommended fixes
Tests performed and their results

Rules:

Do not claim a test passed unless it was executed.
Clearly report uncertainty.
Do not modify unrelated files.

This creates a consistent starting point for everyone on the team.

Step 3: Configure sharing and permissions

Make the configuration available to the intended teammates through your chosen agent platform or internal registry.

However, sharing an agent does not mean sharing every resource it can access. Repository permissions, credentials, execution environments, and other sensitive resources should have their own access controls.

Give each agent only the permissions it needs.

Step 4: Keep task context separate

The agent's permanent instructions should not contain every detail of every assignment.

Instead, provide task-specific information when assigning work:

Task: Review the authentication changes.

Acceptance criteria:

Invalid credentials are rejected.
Existing valid login flows continue working.
Relevant automated tests pass.

Scope:

Review authentication-related changes only.
Report issues before proposing broader refactoring.

This lets multiple teammates reuse the same agent while working on different tasks.

Step 5: Test the configuration with multiple teammates

Before rolling it out across the team, test it against several representative tasks.

Measure:

Whether different teammates receive consistent results.

Whether the agent follows the defined scope.

Whether its findings can be verified.

How often instructions need clarification.

Whether sensitive resources remain properly protected.

Version the instructions where possible, and review changes before distributing them to the whole team.

Step 6: Decide when one agent is no longer enough

A single agent is often appropriate for a focused, well-defined task.

A multi-agent workflow may be more suitable when work requires distinct responsibilities, sequencing, or coordination between agents.

Start simple. Add complexity only when the workflow demonstrates a real need for it.

Final thoughts

Building a shared agent is not just about reusing a prompt. It requires clear responsibilities, consistent instructions, appropriate permissions, task-specific context, and a reliable review process.

Question for developers: How do you maintain shared agent instructions across your team today: version-controlled configuration files, an internal agent registry, or a dedicated agent-management platform?

What Makes a Postman Alternative Truly AI-Native?

Steve Kom — Tue, 22 Sep 2026 08:00:28 +0000

AI-native API development is becoming more than just adding an AI assistant to an API client.

For me, an AI-native Postman alternative should connect the whole workflow:

API design and OpenAPI
Testing AI/LLM endpoints
Mocking and automated tests
Documentation
Collaboration
CLI and CI/CD workflows

I’ve been looking at Apidog as one example, since it combines these parts in a single API development workflow.

With more developers building around LLM and inference APIs, what would actually make an API tool “AI-native” rather than simply adding AI features to an existing Postman-style client?

DEV Community: Steve Kom

How to Build a Shared Agent That Every Teammate Can Use

What Makes a Postman Alternative Truly AI-Native?