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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAgent Experience (AX) is the design and engineering of how AI agents discover, select, and use software. It can become a competitive advantage if agents reliably choose a product and complete useful work through it—but current evidence does not prove a general increase in revenue, retention, or market share from AX investment.
For software teams, the practical task is to make products both discoverable and usable by agents, then test changes against real tasks. That includes documentation, APIs, command-line tools, errors, permissions, and the human controls around consequential actions.
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What is Agent Experience (AX)?
AX describes the experience an AI agent has when it tries to find, choose, and use a product or platform. Microsoft’s 2026 framing treats this as an operational design question: when given an open-ended task, does an agent choose your technology, and when instructed to use it, can it operate the current interfaces correctly?
That scope is wider than a chatbot’s conversational style. An agent may read documentation, call an API or SDK, run CLI commands, use an extension or protocol, interpret an error, or interact with a human-facing interface. Each of those surfaces can affect whether the task succeeds.
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Salesforce describes AX as both designing digital environments for agents and designing agents so their work supports people’s goals. In its April 29, 2025 explanation, Salesforce Chief Experience Officer Kat Holmes characterized the aim as usability both for agents and of agents. In practice, that means a product must be navigable by software while still delivering a result that makes sense to the person who asked for it.
How can you tell whether software is agent-friendly?
Measure two separate things rather than treating “agent-friendly” as a single quality:
- Discovery, or propensity: Given a task that does not name your product, does the agent find and choose it?
- Execution, or efficacy: When told to use your product, does the agent select the right current interface, complete the task correctly, and produce an acceptable result?
Then evaluate the quality of the outcome and the resources required to produce it. A task that technically completes but leaves incorrect configuration, stale output, or excessive tool and model costs is not a good AX result.
Microsoft’s 2026 evaluations show why advice that sounds sensible still needs testing. In one SPFx upgrade evaluation on Windows, five runs of GitHub Copilot Chat with Claude Sonnet 4.6 passed 30 of 80 configuration checks before a change. Instructing the agent to use the CLI for Microsoft 365 raised the result to 75 of 80 checks. Microsoft then examined the agent’s behavior and improved release notes; it reports that later runs used the CLI without an added skill.
A separate Microsoft evaluation found a different outcome: adding JSON input mode to a CLI led Claude Haiku 4.5 to complete two of five deployments. Regular arguments worked in all five runs for every tested agent profile. The JSON-mode approach also cost four to eleven times more per task in that evaluation.
In another cost comparison, Microsoft reported that Claude Sonnet 5 had 33% lower per-token pricing than Sonnet 4.6, yet cost 3.7 times more per run for SPFx code upgrades in GitHub Copilot Chat. That comparison covered three scenarios and 15 runs per model. The lesson is not that one model or input style is generally better; it is that token rates alone do not tell you the cost of completing a task.
How should a team evaluate AX changes?
Use a controlled loop that separates a promising idea from a demonstrated improvement. Choose tasks that represent actual user goals, and record enough detail to reproduce the comparison.
- Choose representative tasks. Include open-ended tasks to test whether an agent finds the product, plus explicit-use tasks to test its interface and task execution.
- Run a baseline. Record completion, correctness, outcome quality, failures, and model and tool costs before changing instructions, documentation, or interfaces.
- Change one surface at a time. For example, revise a CLI error or add a specific warning to documentation, rather than changing several things at once.
- Repeat the runs under matched conditions. Keep the model, task, harness, operating system, and other material conditions consistent, and report them with the results.
- Inspect traces and failure modes. Find out whether the agent failed to discover the right path, misunderstood an interface, received stale output, or made an error while carrying out the task.
- Keep changes that improve the measured result. Compare quality as well as completion and cost; an extension or instruction should be evaluated against the baseline rather than assumed to help.
Microsoft’s examples are vendor evaluations, not a universal benchmark. Their value is methodological: an intervention can improve performance, do nothing, or make results worse, so teams should test the changes they expect agents to benefit from.
Which software surfaces shape agent experience?
Documentation and discovery
Agents may consult documentation while completing a task, so clear, current instructions can influence behavior without waiting for a model change. Make supported paths, product capabilities, and failure-prone alternatives easy to identify. Microsoft’s evaluations include a case where a specific warning about a failing approach helped more than a vague tip; they also show that adding another documentation source does not automatically improve results. Treat both as task-specific observations, not universal rules.
APIs, SDKs, CLIs, and errors
Response formats, naming, versioning, error messages, and command syntax are part of the product interface. Prefer predictable behavior and explain failures in terms that help an agent recover. Verify outputs against the intended current state: Microsoft describes an outdated scaffolder result that an agent interpreted as success, and its CLI evaluation shows that a new input mode can perform worse than ordinary arguments.
Extensions, instructions, and starter projects
Skills, instruction files, custom agents, and generated starter projects can guide an agent or give it a more useful path through a product. They also introduce another component that can be missing, stale, or ineffective. Test the extension or instruction against a baseline, and check whether the agent actually loads and follows it.
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OpenAI reported in 2026 that more than 60,000 open-source projects and agent frameworks had adopted AGENTS.md since its release in August 2025. That is a company-reported count of ecosystem adoption, not evidence that using the convention improves outcomes in every repository.
What do protocols solve—and what do they not solve?
Protocols can make it easier for agents to connect with tools and data, but different protocols address different interoperability needs. Google’s March 18, 2026 developer guide describes the Model Context Protocol (MCP) as a way to connect agents with tools and data without creating and maintaining custom integration code for each endpoint. The guide also discusses A2A, UCP, AP2, A2UI, and AG-UI; it recommends introducing protocol support as requirements emerge rather than adopting everything at once.
The 2025 AI Agent Index, presented at FAccT ’26, documented MCP support in 20 of the 30 agent systems in its sample. It also recorded chat interfaces in 14 of 30 agents and visual composition interfaces in 8 of 13 enterprise agent-building platforms. These counts describe the systems documented in that index, not the entire market or the effectiveness of any protocol.
OpenAI says the Agentic AI Foundation provides a neutral home for shared standards and lists MCP and AGENTS.md among contributed projects. That signals an effort to support common infrastructure; it does not mean one protocol handles every task or removes the need to design usable product interfaces.
How do human oversight and trust fit into AX?
Reliable agent use does not require unrestricted autonomy. Salesforce’s order-change example illustrates how one customer request may involve identity, shipping information, product records, order history, and a delivery service. If those systems are inconsistent, an agent’s failure becomes a problem for the person waiting for help.
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Anthropic’s August 4, 2025 framework identifies the tension between useful autonomy and human oversight, especially before high-stakes actions. Its description of Claude Code cites read-only permissions by default, approval before code or system modifications, and visible plans that a person can redirect. The framework also warns that an agent may over-interpret a request such as “organize my files,” and that information retained across tasks can leak between organizational contexts.
Translate those concerns into product requirements: define what an agent may read or change, identify actions that require confirmation, make intended actions visible, and provide a clear recovery path after failure. Consider whether information from one task or user context can influence another. Those controls determine whether task completion is safe and aligned with the person’s intent, not merely whether an agent can act.
Why might AX become a competitive advantage?
If an agent routinely selects one product over alternatives, or completes a task more reliably through one product’s interfaces, users may be more likely to encounter or depend on that product through agent-mediated work. That makes discoverability and execution plausible sources of advantage, especially as agents take on tasks that cross documentation, tools, and services.
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But the competitive claim remains a thesis, not an established business result. Microsoft makes the strategic implication explicit, while the sources here do not establish a cross-industry causal lift in revenue, retention, or market share from AX work. Teams should therefore treat AX as a product-quality and distribution question to evaluate with task evidence—not as a guaranteed return or a reason to add every new agent integration.
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