AI Agent Design Starter Kit (personas, memory, tool use)

Welcome — purpose and practical outcome

This starter kit helps you rapidly prototype multi-step AI agents that combine persona, memory, tool use, and human oversight to accomplish real tasks without wandering, leaking data, or making unsafe decisions. Use the templates and checklists here to move from an idea to a testable agent prototype within hours, then iterate toward production readiness.

Quick prototyping path (use this as a recipe)

1. Pick a clear, measurable task and one primary success metric (e.g., "Extract and summarize contract clauses; success = 90% clause recall on held-out set").
2. Choose a persona that constrains behavior and tone (see persona template).
3. Select memory pattern(s) for the task (see memory patterns).
4. Define exactly which external tools the agent may call and how (tool contracts).
5. Create 3 scripted test flows: a happy path, an error path, and a malicious-input path.
6. Run the tests, score them with the evaluation heuristics, and refine the agent rules, prompts, or tool contracts.

Persona template (copy and adapt)

Use persona constraints to limit style, authority, and permitted actions. Keep personas short and prescriptive.

• Name: (identifier, e.g., ContractAnalyst v0.1)
• Purpose: One-sentence statement of task and boundary.
• Authority: What decisions the agent may take autonomously vs. must escalate to a human.
• Tone & style: Short, formal, plain-language, etc.
• Data handling rules: What data may be stored in memory, masked, or discarded.
• Failure mode: Default action on uncertainty (e.g., ask for clarification, escalate, or abort).

Example (brief): Purpose: "Identify and summarize indemnity clauses." Authority: "May flag candidate clauses and propose summaries; must not autonomously approve any legal text."

Memory & state patterns

Choose memory patterns based on task length, privacy risk, and required context persistence.

• No persistent memory (stateless): Suitable for one-off tasks or high privacy sensitivity. Agent receives only immediate context.
• Short-term working memory: Stores step-level state for a single session (e.g., conversation history, current plan). Expires at session end.
• Episodic memory: Stores discrete events or decisions from prior sessions to help follow-ups (e.g., prior approvals). Requires retention policy and redaction rules.
• Semantic/long-term memory: Curated knowledge useful across tasks (e.g., user preferences). Protect with encryption and access controls.

For each chosen memory store, define: retention period, who can read/write, redaction rules, and audit logging requirements.

Tool interface contract (pattern)

Define a minimal contract for each external capability the agent can use.

• Tool name: e.g., DocumentSearchAPI
• Purpose: What the tool provides (e.g., retrieve top-N passages for a query).
• Inputs: parameter names, types, and validation rules (e.g., query:text, filters:list of doc-ids).
• Outputs: structured response format (e.g., [{id, snippet, score, source}]).
• Rate limits / cost constraints: maximum calls per action and preferred batching.
• Security & privacy: whether inputs may include PII, encryption requirements, retention rules.
• Error handling: expected error codes and agent fallback behavior (retry, degrade, escalate).

Keep contracts machine-readable if possible (JSON Schema) and enforce them at the adapter layer so the agent logic stays simple.

Scripted test flows (templates)

Create three canonical flows and run them after any change.

1. Happy path: Clear input, expected results, time/latency targets.
2. Error path: Missing data, tool failure, API timeouts — verify graceful degradation and clear error messages to users.
3. Adversarial/malicious path: Inputs designed to provoke hallucination, data-exfiltration attempts, or escalation abuse. Verify agent refuses or flags suspicious requests.

Each flow should include: initial prompt, expected tool calls (and parameters), expected agent response, and pass/fail criteria.

Evaluation heuristics and scoring

Score runs with straightforward metrics to prioritize fixes:

• Correctness (0–5): Are outputs factually accurate against an authoritative gold set?
• Relevance (0–5): Does the response address the user's core need without drift?
• Safety & compliance (0–5): Any confidentiality breaches or forbidden actions?
• Tool usage efficiency (0–5): Minimal required calls, correct batching.
• Robustness (0–5): Behavior under partial failures or noisy inputs.

Record scores and trends. Prioritize fixes with the highest potential risk (safety, compliance) first.

Deployment readiness checklist

• Persona and authority rules documented and reviewed by stakeholders.
• Memory rules, retention, and access controls defined.
• Tool contracts implemented at adapter layer with validation and rate limits.
• Audit logging and observability enabled for tool calls, decisions, and escalations.
• Automated test flows with pass/fail criteria are runnable and stored.
• Human-in-the-loop escalation paths tested and contactable.
• Data privacy and compliance review completed for inputs/outputs and memory stores.

How to iterate fast (practical tips)

• Start with a narrow task and a strict persona. Widen scope only after reliable results.
• Keep tools simple and observable. Prefer read-only capabilities early.
• Use short synthetic datasets for rapid automated tests before running on real data.
• Log every tool call and include context identifiers to reconstruct decision traces.
• Run adversarial tests regularly and add failure cases to unit tests.

Next steps & tailoring

This playbook is a starting structure. Consider turning persona templates, test flows, and the deployment checklist into interactive forms so teams can capture prototypes, run tests, and save results into the platform. Groups with shared needs may package this starter kit as a reusable domain or toolkit and adapt memory, contract, and compliance rules to their environment.

Appendix: quick examples

Example evaluation pass criteria for "extract indemnity clauses": 3/5 for correctness means 70% of clauses found and summarized acceptably; safety score must be 5/5 (no PII leakage).

Example tool contract note: DocumentSearchAPI must never accept raw attachments that contain unredacted PII; attachments must be pre-scrubbed or routed through a redaction microservice.