Technology is only as useful as it is usable. Users will only discover a product’s innovative features if it’s designed well enough for a user to first find and then use them. This is just as true with agentic AI as anything else — even if the agent’s doing a good portion of the work.

Agent experience (AX) is an emerging discipline. It refers to the set of design, engineering, and practices that make services easy, reliable, and safe for AI agents to discover, understand, and consume. With user experience (UX), the focus is on helping users understand the product and how it will solve their problems. With AX, the focus is on machine readability, predictability, security protocols for non-human clients, and clear machine-centric metadata. A healthy AX lets an agent quickly and reliably answer: “What does this service do, how can I connect to it, what are the protocols, and what will happen when I call it?”

Over the last year, developers and designers have converged on a handful of practical patterns, such as:

• Service descriptions (OpenAPI/MCP)
• Discovery and registries
• Agent-friendly onboarding flows
• Agent-first authentication and authorization
• A lightweight MCP server layer
• Development ergonomics that treat agents as first-class consumers

This is just the start when it comes to improving agentic experience. Below, we’ve put together ten tips that extend the above areas to help your organization make the most of AX. These tips should aid agents who consume internal information systems, knowledge bases, data, and APIs, as well as those who consume external-facing APIs.

1. Make Your Capabilities Explicit and Consumable

Agents work best when they’re able to systematically inspect what a service offers. That means publishing machine-readable descriptions using accurate OpenAPI or equivalent interface contracts or the richer tool-and-context descriptions used by MCP (Model Context Protocol) when appropriate.

A concise, canonical description helps agents reason about a tool’s inputs, outputs, side effects, and constraints without wasting time on unnecessary trial and error. It also helps to make prompts less brittle — an agent that can read a service schema can compose more accurate calls rather than having to guess parameter names or payload shapes. The MCP ecosystem is expressly designed for this kind of capability sharing and providing primitives that map well to agent tool use.

2. Provide Discovery and a Registry

Human developers usually learn about services through docs, Slack, or colleagues. In contrast, agents need programmatic discovery. A registry could assist here. A registry is a machine-readable index of tools and capabilities that is either centralized for a product or federated across an organization. It lets agents query to see what tools are available, their versions, and endpoints, and obtain metadata about connections.

Registries should expose searchable metadata like capabilities, rate limits, expected latency, required scopes, and trust level, helping agents pick the right tool for a task or look elsewhere if a tool is unavailable. Recently proposed discovery protocols and agent registries show this is now a vital component of agent ecosystems.

MCP servers provide machine-readable schemas, normalize interfaces, and expose metadata like capabilities, rate limits, expected latency, required scopes, and trust level, making it easier for agents to reason about which tools to call. To help improve discoverability, capability providers also should add their MCP servers to public MCP registries.

On the other hand, agent builders should make sure that their agents can connect to these registries programmatically via APIs, surfacing available capabilities automatically, and selecting the right tool for a task. By combining MCP-enabled registries with agent-aware integration, both service providers and agent developers can create a more seamless, automated agent ecosystem.

3. Ship a Small MCP Server or Gateway

Not every service needs a full MCP implementation, but a lightweight MCP server or gateway in front of your APIs can pay off in dividends. MCP gateways can present normalized schemas to agents, mediate authentication and authorization, and negotiate between an agent-friendly contract and your internal APIs.

This layer allows you to evolve internal products and services without breaking agents and to centralize policies like rate limiting and telemetry. Cloud providers and platform vendors are starting to provide MCP-compatible gateways and agent-core gateways that do exactly this, which indicates that it’s a practical production pattern.

4. Rethink Authentication

Traditional human flows — like passwords or interactive multi-factor authentication (MFA) — aren’t useful for agents. Authentication for agents should rely on automated, verifiable identity mechanisms that streamline human interaction. Machine-to-machine identity flows, such as OAuth 2.0 client credentials or mutual TLS, provide strong authentication for agents operating within trusted systems.

Use short-lived credentials issued by a secure identity provider to minimize risk exposure and ensure revocability. Each agent should have its own unique identity rather than sharing credentials across environments. Consider layered authentication approaches — for example, using attestation tokens to verify the integrity of the agent runtime or leveraging workload identity federation when agents run across cloud or hybrid environments.

The goal is to ensure that every agent interaction can be cryptographically tied back to a trusted identity, with authentication methods designed for automation, auditability, and minimal human dependency.

5. Treat Authorization as Contextual Policy

Authentication proves who is calling. Authorization decides what the caller can do. For agent use cases, policies should accept contextual inputs. These include the intent of the action, the originating agent’s trust level, user consent metadata, environmental risk signals, and recent behavior.

Using a policy engine, a central authorization decision service, means you can change rules without updating agents, and you can log every decision for later analysis. Separating policy also makes it possible to run safe “what if” simulations before changing production rules. To this point, emerging MCP best practices recommend coupling MCPs and gateways with a policy layer to enforce these controls at call time.

6. Design Onboarding That’s Automatable

Onboarding for agents should be an API-first experience. Developers shouldn’t have to dig through documentation just to see how a product operates. Instead, make it as easy as possible for developers to try your agents by giving teams a sequence of steps that can be executed by CI/CD or infrastructure as code.

An example agentic flow that could be automated might be:

• Register the agent
• Mint credentials
• Bind scopes
• Attach roles
• Annotate metadata (such as owner, environment, expected time to live (TTL))

Providing sample agent manifests that include capabilities, required scopes, and example requests also makes it faster and easier to get started with an agent. Automatable onboarding means fewer manual mistakes and faster production rollouts. It also encourages reproducible deployments where the exact configuration that an agent used can be inspected later for debugging or compliance.

7. Surface Operational Characteristics and Limits

Agents should be able to reason about non-functional properties. Publishing expected latencies, rate limits, costs, idempotency guarantees, and common error codes for services helps agents search for the best tools for their needs.

When agents can search for lower latency or cheaper tools for specific tasks, they are able to make more efficient and robust plans. Providing explicit idempotency strategies, like which operations are safe to retry and how to use idempotency keys, is also important. These operational signals should be part of the service metadata that an agent can read during discovery.

8. Provide Enriched Examples and Canonical Usage

Example-rich docs are helpful for both humans and agents. For agents, including canonical JSON payloads, minimal and maximal request examples, and well-typed responses with clear error examples, lets them know precisely what to expect.

It’s a good idea to offer a suite of example prompts and agent-centric “how to” snippets that show the best way to accomplish common tasks like search, summarize, or create a draft, then ask for human approval. This lets users with less technical experience use your agents along with more experienced developers. Because agents reason programmatically, annotated examples with field-by-field explanations are particularly useful. They help to reduce hallucinations and prevent agents from misusing an endpoint.

Documenting common workflows using a specification like Arazzo could provide an additional layer of clarity and structure. By modeling workflows declaratively, you can capture sequences of operations, interdependencies between steps, and expected inputs and outputs in a machine-readable way. This gives agents a deterministic “recipe” to follow, reducing errors and misinterpretation, while also providing humans with a high-level guide for accomplishing complex tasks.

Workflows expressed in Arazzo can include canonical examples, minimal and maximal payloads, and annotated field explanations, reinforcing best practices and helping agents avoid incorrect assumptions about data. These workflow specifications can also serve as input for automated code generation, interactive documentation portals, or testing frameworks, allowing both developers and agents to explore and execute tasks reliably.

By bridging the gap between human intent and agent execution, enriched workflow documentation increases usability, consistency, and confidence, making your APIs more approachable and safer for both highly experienced users as well as those with less technical experience.

9. Log, Trace, and Offer Replayable Traces for Debugging

When agents call services, failures often happen in multiple steps. Centralized logs and distributed traces that tag requests with agent identifiers, intent IDs, and causal chains help engineers reconstruct what an agent did and why.

Providing a replayable debug mode where a sequence of agent actions can be replayed against a sandbox or with throttled rate limits is invaluable for reproducing issues without risking production side effects. Instrumenting MCP gateways and policy engines gives you high-fidelity observability for agent-driven workflows.

10. Evolve Agentic Safety and Trust Iteratively

Agents can take actions at scale. It’s essential to start with conservative defaults like minimal scopes, request limits, and dry run modes where side-effecting operations propose changes rather than executing them. Implement human-in-the-loop patterns for high-risk operations and provide clear escalation channels for rapid human intervention in case something goes wrong.

As you get real-world examples derived from actual usage, you’ll be able to widen the set of permitted behaviors for trusted agents while hardening defenses for new or unknown agents. This progressive trust model balances velocity with safety, in keeping with many modern machine identity best practices.

Creating a Quality Agentic Experience

Building good agentic experience is not a one-off project but an ongoing discipline that combines API design, machine-first authorization and discovery primitives, operational transparency, and governance. To start improving your AX, start small. Publish accurate interface descriptions, add a discovery endpoint, and centralize authn/authz services.

From there, an MCP gateway, agent registry, and policy engine will let you scale agent integrations safely. Over time, you’ll learn which signals matter most for your agents — latency profiles, cost constraints, prompt templates, or privacy classifications — which can then be encoded into the UX that an agent uses to choose and call your service.