Most agent stacks give the model a long menu of disconnected JSON tools and hope it chooses correctly. Plasm turns each API into a typed map of business objects, relationships, and allowed actions, then exposes that map through one compact language the agent and runtime both understand. The result is cross-system work that can be taught, checked, reviewed, and executed with far less improvisation.
A task like triaging GitHub issues, checking Slack context, updating a project tracker, and drafting a customer reply can become a plan you can dry-run, validate, approve, and trace, while Plasm handles authentication, pagination, result shapes, execution order, and vendor-specific details underneath.
JSON tool calls can pass arguments, but they make the model learn every vendor's naming, pagination, response envelope, and parameter convention directly. That works for demos, but it becomes fragile when a workflow touches multiple services or needs approval before data changes.
Plasm approaches the problem as a product contract. APIs are modeled as useful business entities, relationships, and actions. The agent describes intent against that model, while the runtime maps the request to the right backend calls and normalizes what comes back.
The practical benefit is a cleaner boundary between the model and the systems it controls. Plasm can show what will be read, what will be computed, what will be changed, and which steps need review before anything touches a live backend.
search_tools · list_tools · schemas · host prose · tools/call · full JSON
Many schemas and conventions stay in the prompt, turn after turn.
discover · add context · compile plan · dry review · run
The agent, validator, and executor share one typed contract: discover, plan, review, run.
Same task: the left bar is prompt and vendor mechanics; the right is a reviewable plan plus selected results.
Schemas, examples, call conventions, and payload details the model should not have to memorize.
One typed surface, explicit review points, and returned references the agent can safely reuse.
Plasm is designed for teams that need agents to work across real software stacks without brittle custom glue. It gives the model fewer things to memorize and gives teams clearer places to validate, approve, and audit work.
Plasm teaches APIs as compact entities, relationships, and actions instead of long, repeated endpoint definitions. As integrations grow, the agent gains vocabulary without relearning a new tool convention for every vendor.
Real automation often reads from several systems, summarizes what was found, and then applies changes in the right order. Plasm turns that work into a plan the host can inspect before execution.
Typed catalogs, deterministic validation, and domain-aware correction feedback close the gap between what the model asks for and what the system is allowed to do.
Catalog authoring turns raw API documentation into the business model an agent should actually use: entities such as issues, repositories, users, messages, documents, tasks, and comments, plus the relationships and approved actions that connect them.
Runtime execution uses the same catalog to validate requests, follow relationships, select fields, manage pages, hydrate missing data, cache results, stage side effects, and dispatch calls to REST, GraphQL, EVM, or future backends. The agent works with business intent; Plasm absorbs the vendor mechanics.
The outcome: smaller prompts, fewer retries, and plans you can review before data changes.
Author once as a reusable catalog; serve the same model to agents, MCP clients, HTTP execution, REPL workflows, and a generated CLI. That keeps developer tooling and agent behavior aligned.
A catalog turns GitHub into business objects, relationships, and actions the agent can use consistently, while the runtime keeps the backend mapping precise.
The main contrast is not connector count. It is whether your agent is learning a long list of unrelated tool schemas, or working through a typed business contract that can be validated, governed, and reused across systems.
Evaluate Plasm next to raw schema and JSON tool stacks on one question: does each new integration add more prompt bulk and more vendor-specific rules for the model to memorize, or does it extend a shared operating language?
The Plasm answer is a reusable catalog, a compact agent-facing language, and a runtime that handles the mechanics: authentication, pagination, result normalization, execution order, approval boundaries, and traces.
Plasm gives teams practical control points for enterprise automation. Reads, searches, pages, projections, relationships, and writes become typed outcomes the host can inspect, route through policy, and trace back to the catalog that authorized them.
Mistakes such as wrong fields, invalid filters, or disallowed actions surface as structured guidance before live credentials are used.
Relationship traversal, extra fetches, pagination, and follow-up reads follow catalog rules, so the execution path is explainable.
Each business action maps to explicit backend rules, so “what the agent asked for” and “what the API received” stay connected.
Policy and approval gates can attach to the same actions the agent already reasoned about, close to the work they govern.
Plasm is for teams that want reusable, governed agent automation instead of brittle one-off tool chains. It gives agents a compact language for business intent, gives developers one authored integration model, and gives operators a plan they can review before live systems change.