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Agent Experience (AX) is how well AI agents can discover, choose, and use a product or platform. Designing for it means making interfaces, instructions, and safeguards work for agents while ensuring their actions lead to good outcomes for people. It may become a competitive advantage if agents reliably select and use some products over others, but current evidence does not establish a general business payoff from AX investment.
What is Agent Experience (AX)?
Microsoft defines AX around an agent’s experience discovering, choosing, and using technology. Salesforce frames the idea more broadly: design both the environment agents work in and the agents themselves so their work serves people’s goals.
In practice, an agent is another kind of software user. It may read documentation, interpret an error, select a tool, and act through an API, SDK, command-line interface (CLI), protocol, or human-facing interface. AX therefore extends beyond the tone or convenience of a chatbot. It includes the product surfaces an agent encounters and the conditions under which it is allowed to act.
The strategic case is plausible: if an agent repeatedly chooses one product for a task, or completes that task more reliably through one interface, people may be more likely to encounter that product through agent-mediated work. Microsoft makes this implication explicit. But the available sources do not show that AX work has already produced a general increase in revenue, retention, or market share across industries.
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How do you tell whether software is agent-friendly?
Separate two questions that are easy to blur together. Microsoft calls them propensity—whether an agent finds and chooses the technology for an open-ended task—and efficacy—whether it uses the technology correctly when asked to use it. A product can do well on one and poorly on the other.
- Discovery: Given a task that does not name your product, does the agent find and select it?
- Execution: Can the agent follow the current supported path and complete the intended task?
- Outcome quality: Is the result correct and useful, rather than merely an apparent success?
- Cost: What model and tool resources did the task consume?
- Human impact: Was the result aligned with the person’s intent, and could they understand or control the consequential actions?
These dimensions matter together. An agent can choose a product but use it incorrectly; it can also complete a task while taking an unnecessarily expensive route or creating a result that a person must repair.
What Microsoft’s evaluations show—and what they do not
Microsoft’s reported evaluations illustrate why AX conventions should be tested rather than treated as universal rules. The figures below are results from specific tasks and setups, not general benchmarks for agents or products.
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| Evaluation | Reported result | What it illustrates |
|---|---|---|
| SPFx upgrade configuration checks | In five runs on Windows, GitHub Copilot Chat with Claude Sonnet 4.6 passed 30 of 80 checks before an intervention. After being told to use the CLI for Microsoft 365, it passed 75 of 80 checks. | A specific, task-relevant instruction can change whether an agent finds and follows a working path. |
| CLI deployment input modes | In a separate evaluation, Claude Haiku 4.5 completed two of five deployments with JSON input mode. Regular arguments worked in all five runs for every tested agent profile. | A feature that appears easier for an agent may perform worse than an ordinary interface option. |
| Cost of that JSON-mode approach | Microsoft reported 4x to 11x more model cost per task in the same evaluation. | Completion and cost should be evaluated together. |
| SPFx upgrade model comparison | Across three scenarios and 15 runs per model in GitHub Copilot Chat, Microsoft reported that Claude Sonnet 5 had 33% lower per-token pricing than Sonnet 4.6 but cost 3.7x more per run. | Lower token pricing does not necessarily mean a lower cost to complete the work. |
In the first evaluation, Microsoft traced the agent’s behavior and improved the release notes; it reports that subsequent runs used the CLI without an added skill. The example supports a practical lesson: inspect what the agent actually did before deciding which instruction or interface to change. It does not show that adding a warning, a skill, or a particular CLI will help every product.
How to evaluate an AX change
- Choose representative tasks. Include realistic tasks that test discovery as well as tasks where the agent is explicitly told to use your product.
- Record a baseline. Run the tasks before changing documentation, interfaces, or agent extensions. Track completion, correctness, outcome quality, and cost.
- Change one surface at a time. For example, revise an instruction, change an error response, or add an extension. Isolating changes makes it easier to see what affected the result.
- Repeat runs under controlled conditions. Use the same task, model, harness, and environment for the baseline and comparison. Record the model, operating system, task, and run count alongside results.
- Inspect traces and failures. Look for incorrect assumptions, stale instructions, unnecessary tool calls, failed actions, and apparent successes that did not produce the intended state.
- Keep changes that demonstrate improvement. Compare both task outcomes and cost; a change that adds complexity or expense without improving useful results may not be a win.
Microsoft’s examples include a specific warning about a failing approach outperforming a vague tip in its test, while adding another documentation source did not necessarily help. Those are findings from individual evaluations, not rules to apply without testing.
Which parts of a product shape AX?
Documentation and discovery
Agents may consult documentation while carrying out a task, so current, findable guidance can affect how they use a product. Make supported paths, product selection cues, version-sensitive instructions, and known failure-prone alternatives easy to locate. Ensure that guidance reflects what the product does now: agents can confidently repeat outdated instructions.
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APIs, SDKs, CLIs, and errors
Treat interface names, response structures, versioning, error messages, and input modes as part of the agent’s working environment. An error should help identify what failed and what recovery path is available. Microsoft describes an outdated scaffolder output that an agent interpreted as success, as well as the deployment results in which JSON input mode underperformed regular arguments. Those examples make verification important: check the resulting state, not only whether the agent reported success.
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Extensions and shared instruction files
Skills, instruction files, and custom agents can guide or extend an agent’s environment, but they can also add maintenance work or fail to load. Compare them with a baseline rather than assuming more agent-specific material is better.
OpenAI reports that more than 60,000 open-source projects and agent frameworks had adopted AGENTS.md since its release in August 2025. This is a company-reported adoption count, not evidence that the convention improves outcomes in every repository. OpenAI also says the Agentic AI Foundation provides a neutral home for shared standards and lists MCP and AGENTS.md among its contributed projects.
Protocols and integrations
Protocols can help agents connect to tools, data, and other systems, but different protocols address different integration needs. Google’s March 18, 2026 developer guide describes the Model Context Protocol (MCP) as a way to connect agents to tools and data without writing and maintaining custom integration code for each endpoint. The guide also discusses A2A, UCP, AP2, A2UI, and AG-UI, and recommends adding protocol support as requirements emerge rather than adopting everything at once.
The 2025 AI Agent Index reports MCP support in 20 of the 30 agent systems it documents. In the same index snapshot, 14 of 30 systems had chat interfaces, while 8 of 13 enterprise agent-building platforms had visual composition interfaces. These counts describe the index’s documented sample, not the entire market.
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Agent usability is not the same as unrestricted autonomy. Salesforce’s human-centered framing asks whether agents’ work supports people’s goals. Its order-change example shows the coordination a seemingly simple task may involve: customer identity, shipping details, product data, order history, and a delivery service. When those systems are inconsistent, the effects can reach the person waiting for help.
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Anthropic’s August 4, 2025 framework describes the tension between agent autonomy and human oversight, especially before high-stakes actions. In its description of Claude Code, Anthropic says permissions are read-only by default and users approve code or system modifications. It also describes visible plans that users can redirect. These are Anthropic’s framework and product choices, not a guarantee that every agent has equivalent controls.
Use concrete questions to assess trust and control:
- What information can the agent read, and what can it change?
- Which actions require confirmation, and can a person see what the agent intends to do before they happen?
- Can a person interrupt or redirect a task, and are consequential actions observable and reversible?
- Does an error explain what failed and how to recover?
- Could information retained from one task or user context flow into another?
- Does the agent’s interpretation of a broad request stay within the person’s likely intent?
Anthropic warns that an agent could over-interpret a request such as “organize my files,” and that retained information can leak across organizational contexts. Permission boundaries, visible plans, and context isolation are therefore part of AX quality—not extras to consider only after task completion is optimized.
Why AX may become a competitive advantage
AX gives teams a way to examine whether agents can find their products, use them correctly, and deliver worthwhile results at an acceptable cost. The advantage, if it emerges, will depend on the task and environment: a product that is easier for an agent to discover or operate may be favored in that context, while reliability, integration effort, maintenance, and human control still matter.
The evidence supports treating AX as a design and evaluation discipline, not a guaranteed growth strategy. Microsoft’s task-level examples show that small interface or guidance choices can have measurable effects in a particular setup—and that intuitive changes can fail. The reported ecosystem counts show activity around shared conventions and protocols, but they do not establish a business return. Teams should make the case for AX with repeated, controlled evaluations of their own tasks and with outcomes that matter to the people using the software.
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