AI Agents for Research — design pattern checklist

Why this checklist matters

AI agents can speed literature scans, automate routine analyses, triage datasets, or assist experimental design — but without clear design patterns they introduce reproducibility gaps, hidden errors, and privacy risks. Use this checklist to design agents that are useful, auditable, and safe in research contexts.

Design checklist (with practical guidance)

1. Define agent scope and output formats

   Be explicit about the agent's goal, inputs, outputs, and acceptable formats. Avoid vague goals like “help with literature”; prefer “extract methods and sample sizes from PDF literature and output a CSV with DOI, method, sample_size, and confidence_score.”

   Acceptance criteria: One-paragraph scope statement plus example input and a sample output file.

2. Data access and permission boundaries

   List every data source the agent will touch (internal datasets, external APIs, private repos). Define who can grant access, what credentials will be used, and how access will be revoked. Specify whether data may be cached, stored, or transmitted externally.

   Acceptance criteria: Access matrix with data sensitivity levels and an explicit consent/approval recordfor each source.

3. Traceability and provenance

   Decide what will be logged: prompts, model version, input IDs, timestamps, retrieved sources, and the agent's decisions. Use structured logs so results can be traced back to specific inputs and model calls.

   Acceptance criteria: Log schema defined and a sample log entry showing prompt, model ID, retrieved sources, and final output reference.

4. Human-in-the-loop escalation points

   Define thresholds or conditions that require human review (low confidence, conflicting sources, PII exposure, safety flags). Specify the role responsible for review and the expected SLA for responses.

   Acceptance criteria: Escalation policy with conditional rules and assigned reviewer roles.

5. Validation & test cases for outputs

   Create unit-style tests, example inputs, expected outputs, and failure-mode checks. Include synthetic edge cases and a reproducibility test that replays recorded prompts and data to ensure stable outputs across model upgrades.

   Acceptance criteria: Test suite with pass/fail criteria, and at least one reproducibility test run recorded.

6. Security, privacy, and compliance checks

   Assess privacy risk (PHI, PII), export control, and institutional policy compliance. Include data minimization, encryption-in-transit and at-rest, and a brief threat model identifying likely misuse scenarios.

   Acceptance criteria: Completed privacy impact checklist and signoff from the relevant compliance owner when required.

Quick design template (copy & fill)

1. Agent name:
2. Purpose / Scope (one sentence):
3. Inputs (types & examples):
4. Primary outputs (format + example):
5. Data sources & permissions:
6. Logging & provenance fields required:
7. Validation tests (list):
8. Escalation conditions & reviewer role:
9. Security & privacy notes:
10. Model / toolchain versions to pin:

Validation checklist: quick tests to run before release

• Run five representative inputs and compare outputs to human-labeled ground truth.
• Run adversarial/edge-case inputs (malformed, ambiguous, missing data) and confirm safe behavior.
• Replay logged prompts and data to confirm outputs are reproducible with pinned model versions.
• Confirm logs contain source identifiers and are stored in an auditable location.
• Verify escalation produces a human review with documented outcome.

Common mistakes to avoid

• Leaving scope vague and letting the agent drift into unapproved tasks.
• Failing to pin model versions or record the model meta-data used for an output.
• Not testing reproducibility after routine model or prompt updates.
• Exposing sensitive data to third-party models without explicit controls.
• Assuming high-confidence output is correct — always provide validation or human review for critical results.

Example agent types

• Literature extractor: scrapes PDFs, extracts methods/results, outputs structured CSV with provenance links.
• Data triage agent: scans incoming datasets for schema, completeness, and common QC failures, then flags for human action.
• Experiment scheduler assistant: suggests experimental parameters based on prior runs, and generates a checklist for lab staff.

Next steps

Use the Quick design template to draft your agent, run the Validation checklist, and keep a short audit trail (logs + test results). Consider converting this checklist into an interactive form to capture design decisions and test results for organizational memory.