Designing Clear Task Interfaces for Agent-Based Development

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Multi-agent development succeeds when each agent has a small, testable responsibility and a precise way to communicate results to others. This article gives a practical checklist and concrete patterns to design well-scoped tasks with explicit interfaces so agents can work independently and recombine reliably.

1. Start by naming the capability and the success criteria

Give each task a short name (e.g., “Extract invoice fields”) and a measurable success condition (e.g., “return payer, amount, and ISO date with ≥95% schema validity”). Success criteria guide decomposition, testing, and when to escalate to a human.

2. Define a single, narrow responsibility

Limit tasks to one coherent problem: parsing, validation, transformation, search, or decision. Avoid mixing responsibilities (e.g., “parse and decide policy exceptions”)—split them into two agents with an explicit handoff.

3. Specify the interface contract

Document inputs, outputs, error modes, and side effects. Keep contracts machine-friendly:

Inputs: types, required/optional flags, example values, maximum sizes (tokens/bytes).

Outputs: structured JSON schema or typed object, required fields, allowed value ranges, and canonical formats (e.g., RFC3339 for timestamps).

Errors & failure states: enumerated error codes, retryable vs non-retryable, and an explicit escalation flag for cases needing human review.

Side effects: any external writes, API calls, or notifications the agent may perform.

4. Use small, explicit data schemas

Provide a minimal JSON Schema (or Protobuf/Avro) for every input and output. Include examples and one-liner field descriptions so agents can validate and normalize before returning results. Example:

{“invoice_id”:”string”,”amount_cents”:”integer”,”currency”:”string”,”date”:”string (YYYY-MM-DD)”}

5. Define the interaction pattern

Choose one communication model and document it: request/response, publish/subscribe events, or blackboard entries. For each, record the message topic, keys used for routing, and ordering/consistency expectations.

6. Make plans and intermediate artifacts inspectable

When tasks are multi-step, require the agent to emit an explicit plan or checklist (steps + success checks) before execution. This makes debugging and human review tractable.

7. Provide deterministic test vectors and acceptance tests

Supply representative inputs (golden files) and the expected output or validation rules. Automate unit tests that fail fast on schema violations, formatting differences, or missing fields.

8. Version interfaces and support backward compatibility

Tag interface versions and design for additive changes only (new optional fields). Require agents to advertise supported versions and negotiate or fail with a clear error when incompatible.

9. Enforce contracts at runtime

Validate inputs on receive and outputs before publishing. Use a lightweight gateway or middleware that checks schema conformance, enforces size/time limits, and annotates provenance (which agent produced the output and which model/version it used).

10. Define observability and auditing fields

Require every message to include a minimal audit header: task_id, agent_role, agent_version, timestamp, and trace_id. This supports replay, debugging, and disagreement resolution.

11. Plan for disagreement and reconciliation

When multiple agents can produce overlapping outputs (e.g., extractors), define a reconciliation policy: deterministic aggregator (first valid), majority vote, confidence-weighted merge, or escalate to reviewer agent/human.

12. Add human-in-the-loop (HITL) gates where needed

For risky or ambiguous outputs, add explicit review checkpoints in the interface: output plus a required review token before downstream side effects are allowed.

13. Example: simple extractor interface

Name: ExtractInvoiceFields v1

Input schema: {“document_id”:”string”,”image_base64″:”string (JPEG/PNG)”}

Output schema: {“invoice_id”:”string”,”total_cents”:”integer”,”currency”:”string”,”date”:”YYYY-MM-DD”,”confidence”:0.0-1.0}

Errors: {“code”:”MALFORMED_INPUT|PARSE_FAILED|LOW_CONFIDENCE”,”retryable”:true/false}

Success: all required fields present and confidence ≥0.85

14. Governance checklist before production

– Schemas and examples committed to repository; contract tests in CI.

– Runtime validation & observability enabled (logs, traces, metrics).

– Reconciliation policy implemented and tested with edge cases.

– HITL flows and escalation paths documented and reachable.

Well-scoped tasks with explicit interfaces reduce ambiguity, make automated validation possible, and keep multi-agent systems maintainable as they grow. Start small, require concrete contracts, and iterate on the schema and tests as real failures reveal missing boundaries.

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