Compare Copilot Studio agent harnesses
Copilot Studio provides three harnesses for different types of work. To select one, consider what the agent must accomplish, how much control you need, and where people access it.
Use the GitHub Copilot harness for reasoning-heavy work
The GitHub Copilot harness supports agents and workflows that complete complex, multistep work. It can interpret a goal, create a plan, call multiple tools, observe results, and adjust when a request changes or an action fails.
This harness is appropriate when the solution needs to:
- Reason through ambiguous decisions.
- Coordinate several tools, data sources, or agents.
- Complete longer business processes that can't be fully scripted in advance.
- Use features such as skills and memory.
You guide these agents with natural-language instructions, knowledge, tools, skills, and boundaries. You don't author every possible conversation route. Agents and workflows powered by this harness use Copilot Credits for usage-based billing.
For example, an accounts-payable agent can read an invoice, locate its purchase order, compare the values, request missing evidence, and route an exception. The correct path depends on the content of each invoice and the results returned by business systems.
Use the standard harness for predictable paths
The standard harness supports rule-based agents and agent flows. It is appropriate when you need structured, repeatable conversations or processes with predictable behavior.
This harness is appropriate when the solution needs to:
- Follow topics and paths that a maker defines.
- Use specific trigger phrases, questions, conditions, and branches.
- Provide tightly controlled responses for well-understood requests.
- Reuse existing standard-harness topics, prompts, or agent flows.
For example, an equipment-return agent can collect an asset number, confirm the employee's location, present a fixed set of return options, and route the conversation according to explicit conditions. The standard harness remains fully supported alongside the GitHub Copilot harness.
Use the Copilot Chat harness to extend Microsoft 365 Copilot Chat
The Copilot Chat harness connects enterprise knowledge to Microsoft 365 Copilot Chat. Employees interact with the solution in their existing Microsoft 365 Copilot experience.
This harness is appropriate when the main requirement is to:
- Ground Microsoft 365 Copilot Chat in organizational content.
- Provide an internal, knowledge-focused experience.
- Keep employees in the Microsoft 365 Copilot interface.
- Respect the user's existing Microsoft 365 permissions when retrieving content.
For example, an onboarding agent can answer employee questions from approved SharePoint sites and policy documents without requiring a separate external agent experience.
Compare the harnesses
| Aspect | GitHub Copilot harness | Standard harness | Copilot Chat harness |
|---|---|---|---|
| Primary use | Reasoning-heavy, multistep processes | Rule-based agents and structured conversations | Extension of Microsoft 365 Copilot Chat |
| Main design approach | Goals, instructions, knowledge, tools, and skills | Topics, prompts, rules, and defined paths | Enterprise knowledge connected to Copilot Chat |
| Adaptation | Can adjust its plan based on results | Follows maker-defined behavior | Focuses on grounded chat responses |
| File-intensive work | Supports creating, editing, and reasoning over files | Not a primary focus | Not a primary focus |
| Publishing | Internal teams or external customers | Internal teams or external customers | Internal teams |
| Billing | Copilot Credits | Standard Copilot Studio licensing | Consumption-based or eligible Microsoft 365 Copilot entitlement |
Note
Harnesses use different billing models. For current pricing and licensing information, see Copilot Studio licensing.
Apply a selection sequence
Use these questions when selecting a harness:
- Does the work require an independent agent, or should it extend Microsoft 365 Copilot Chat?
- Must the agent follow explicit conversation paths, or should it reason dynamically toward a goal?
- Does the work require multiple tools, memory, or recovery from intermediate failures?
- Where must users access the completed agent?
- Which licensing and consumption model fits the organization?
These questions help separate a solution's actual requirements from the appeal of any individual feature.