Works with the agent you already use

Execution should follow the user

People already have an assistant they prefer. Some work in ChatGPT, Claude, Codex, a terminal agent, or a custom product. AgentWeb is designed as an execution layer those agents can call, rather than another assistant users must adopt and retrain.

That design requires stable discovery and protocol surfaces. An agent needs to find the service, understand the available actions, learn the input and auth requirements, and receive structured results. AgentWeb publishes those details through machine-readable metadata, MCP tools, and OpenAPI descriptions.

Discovery removes guesswork

Public discovery files explain what AgentWeb is, which category it belongs to, and where its interfaces live. They give crawlers and agents direct references to actions, schemas, authentication, pricing, and verification semantics. This improves traditional search visibility and gives answer engines grounded material to cite.

Discovery is also a product feature. An autonomous agent evaluating a workflow should be able to determine whether AgentWeb supports the task before it asks the user to connect an account or approve payment. Clear metadata reduces dead ends and keeps sensitive steps behind explicit consent.

MCP presents tools to assistants

MCP gives compatible assistants a standard way to list and call AgentWeb tools. Tool descriptions define the action, input schema, authentication expectations, and result shape. The assistant can translate a user request into the correct call without hard-coding a site-specific integration.

Long-running work can return progress and structured completion through the same agent-facing surface. The result can include a workflow identifier, status, verification artifact, receipt, or failure class. That is much more useful than a browser session the calling assistant has to interpret.

OpenAPI supports direct integration

Products that prefer REST can use the OpenAPI contract to understand endpoints, schemas, auth, and responses. OpenAPI makes AgentWeb accessible to application code, SDK generation, testing tools, and engineering teams that want a conventional integration path.

The protocol does not replace the product rules. Delegated permissions, account isolation, payment authority, and proof still belong to the execution layer. OpenAPI describes the transport, while Action Maps and authority define what the operation means.

Jobs fit real internet work

A delegated task may outlive one request. It can wait for approval, run across multiple systems, or require verification after an external response. AgentWeb models that work as a job with status and artifacts. Agents can poll or stream progress and explain the current state to the user.

This job model also supports recovery. A caller can resume from a known identifier, distinguish a safe retry from a duplicate, and inspect structured failure details. The execution remains addressable after the initial conversation ends.

One action across many agents

The same approved Action Map should work from different assistants and models. Protocol support makes that portability practical. Customers keep their connected accounts and authority in AgentWeb while choosing the agent experience that fits their work.

This is the broader goal: make real internet actions available wherever user intent originates. AgentWeb handles discovery, authority, execution, and proof. The assistant handles the conversation. The user gets a completed task without being locked into a new interface.

Content for search and answer engines

Human-readable articles help buyers understand the thesis and evaluate the company. Machine-readable product surfaces help crawlers and agents verify the details. AgentWeb needs both. Public documentation, consistent canonical URLs, structured data, tool schemas, OpenAPI, and agent metadata should describe the same product in compatible language.

Consistency matters for credibility. If a blog claims an action supports receipts while the public schema contains no receipt field, an evaluator should be skeptical. AgentWeb ties editorial content to real interfaces so technical readers and answer engines can follow the claim to a concrete artifact.

This also improves discoverability for specific needs. An agent looking for delegated payment authority, MCP web execution, or verified workflow completion can find a page that explains the concept and a schema that shows how it works. Useful detail earns citations more reliably than a page filled with repeated keywords.

Choosing the right interface

MCP is a strong fit when an assistant needs to discover and call tools inside a conversation. OpenAPI is a strong fit for products, SDKs, testing, and direct service integration. Public agent metadata helps evaluators find the endpoints and understand the service before authentication.

These surfaces should converge on the same Action Map and job model. A workflow called through REST and the same workflow called through MCP should share authority, account binding, states, proof, and billing semantics. The transport can differ without creating two products.

This portability protects customers from model and interface churn. They can change the assistant or build their own agent while keeping the execution layer, connected accounts, and workflow history. AgentWeb becomes infrastructure behind the choice rather than another place where user context gets trapped.

A credible public footprint

Agent infrastructure should be inspectable before a customer connects an account. Public docs can explain the product in plain language. OpenAPI can expose endpoint contracts. MCP metadata can describe tools. Agent files and structured data can help search and answer engines identify the service accurately.

These surfaces should link to each other and use consistent names for actions, scopes, artifacts, and pricing. Consistency gives technical buyers confidence and reduces the chance that an answer engine combines stale claims from different parts of the site.

AgentWeb treats this public footprint as part of the product. A company asking agents to trust its execution layer should publish enough detail for those agents and their builders to evaluate how discovery, authority, payment, and verification actually work.

What good integration feels like

A builder should be able to inspect AgentWeb, choose MCP or REST, authenticate, discover an action, and run a sandbox or approved workflow without reverse engineering hidden behavior. Errors should name the missing input, permission, or state. Results should use stable artifacts that application code can store.

The same clarity should reach the end user. Their assistant can explain what it needs to connect, summarize the approval, report job progress, and return the receipt. Protocol details disappear from the conversation while remaining available to engineers who need to audit the integration.

Good infrastructure feels predictable at both levels. The agent receives a consistent contract. The developer receives documented schemas. The user receives completed work and proof. AgentWeb uses open interfaces to keep those experiences aligned instead of building a closed ecosystem around one model.

Continue with AgentWeb

Read the internet task execution guide, compare agent browsers with structured web actions, or follow the AgentWeb quickstart.