THE API-FIRST PLAYBOOK / BUILD WITH TODAY’S INTERFACES
Connect the parts. Keep the contract.
Useful agent systems can start with model APIs already available today. Their capabilities come from the whole design: a task contract, permitted tools, relevant context, evaluation and a person accountable for what happens next.
This guide separates that practical starting point from claims of AGI. The explorer below is an interactive browser simulation—not a proprietary inference API, an account or a connected model service.
01 / MAKE THE INTERFACE TANGIBLE
A request. A reviewable plan.
Change the example, edit the JSON and generate an illustrative response. The input stays in this page. No API keys, network calls, model execution or business-system access are involved.
BROWSER-ONLY SIMULATION
Use a goal of 10–500 characters, 1–16 roles and up to 12 tool names. Use invented or non-sensitive examples only.
ILLUSTRATIVE RESPONSE / NOT EXECUTED
Your simulated response will appear here.
Local simulation ready.
A useful interface makes the intended outcome, permitted inputs and action boundary explicit. Its response should distinguish a plan, a draft and an executed action. A fluent answer is not evidence that a task was completed.
02 / THE TASK CONTRACT
Define “done” before “run”.
A task contract is a small piece of engineering discipline that prevents a large amount of ambiguity. Keep it independent of any one model provider, and make it specific enough that another person could review the result.
Goal: describe a bounded outcome rather than a personality or vague ambition.
Inputs: list approved sources, their owners and freshness requirements. Treat retrieved text as data, not new authority.
Tools: give each role only the read or write capabilities it needs. Enforce those permissions outside the model.
Output: specify a schema, evidence requirements, uncertainty and the difference between a proposal and an action.
Stop conditions: define retry limits, missing-input handling and the point where a human takes over.
Evaluation: state what makes an output useful and which failures are unacceptable.
“Prepare a comparison from these approved sources, cite each material claim and stop for review” is a task contract. “Be an autonomous procurement genius” is not.
A model adapter is the boundary between your application and a selected model API. It translates your task contract into the provider’s supported request format and normalises the response. The rest of your system should own permissions, state, retries, logging and release decisions.
// Provider-neutral pseudocode — not a live endpoint.
const contract = validateTask(input);
const context = retrieveApprovedSources(contract.sources);
const draft = await modelAdapter.generate({
instruction: contract.goal,
context,
outputSchema: contract.outputSchema,
permittedTools: contract.readTools
});
const checked = validateOutput(draft, contract);
const evidence = evaluate(checked, heldOutCases);
return prepareForHumanReview(checked, evidence);
// A separate authorised executor handles approved writes.
Do not put model credentials in browser code or ask visitors to paste API keys into a public demo. Keep authentication, rate limits and provider calls in an application backend. Validate structured responses before passing data to another role or tool.
Compose roles only when the separation helps.
A researcher, analyst and reviewer can have different input contracts and tool access. The orchestrator needs a way to identify incomplete work and conflicting results. More roles also mean more messages, latency and failure paths, so compare the design with a simpler single-agent or conventional-software baseline.
Keep memory scoped.
Attach provenance, ownership and a retention decision to stored context. Separate customer and project memory. A fact learned in one workflow should not silently become an instruction or a permission grant in another.
04 / BEFORE A REAL-WORLD RELEASE
A compelling demo is the beginning.
Test the workflow against representative inputs, missing information, adversarial content and tool failures.
Separate the cases used to improve a candidate from the cases used to judge its release.
Measure task success, evidence quality, escalation, cost and latency against an agreed baseline.
Version prompts, models, tools, memory policies and evaluation sets so results can be reproduced.
Agree monitoring, incident ownership and a rollback path before extending autonomy.
Capabilities and data handling vary by model provider, deployment and configuration. Confirm current documentation, commercial terms and operational requirements for the system you are actually building.
Use these current provider and engineering references alongside your own evaluation. Inclusion is educational and does not imply endorsement or a commercial relationship.