Multi-Agent Custom GPT

Multi-Agent Custom GPT: A Practical Blueprint

Multi-agent Custom GPTs let one assistant orchestrate several specialized “skills” (agents) behind a single chat surface. Instead of forcing users to switch chats or paste long instructions, a single GPT routes intent to the right capability—memory management, web scraping, scheduling, or document analysis—then returns a unified response. Below is a field-tested blueprint you can adapt.

1) Core Idea

  • One chat, many skills. Declare multiple functions (“agents”) in your system prompt and action schemas.

  • Router by instruction. Teach the GPT when to invoke each function using clear, minimal patterns (keywords, hotkeys, or exemplars).

  • Tight UX. Provide a concise “Main Menu” and optional hotkeys; support both text and voice.

2) Architecture at a Glance

  • Prompt Brain: Explains capabilities, inputs required, validation rules, and fallback messages.

  • Action Schemas: One per skill (e.g., Memory, WebScrape, Calendar, PDF-QA). Each defines the endpoint, inputs, auth, and response format.

  • Hosted Connectors (optional): A proxy that standardizes API keys and headers, and exposes stable URLs for your actions.

  • Guardrails: Input validation, required fields, graceful errors, and safe defaults.

  • Telemetrics: Capture success/failure, latency, and user corrections for iteration.

3) Defining Agents (Functions) in the Prompt

Provide short, explicit rules the model can follow:

  • Intent cues: “If user asks to store or search personal notes → invoke Memory.”

  • Input contracts: “For Calendar events, require title, start/end, timezone; ask for missing pieces.”

  • Examples: One or two compact example exchanges per agent (request → agent call → expected reply).

Keep examples brief; the goal is reliable routing, not encyclopedic tutoring.

4) Four Foundational Agents to Start With

A) Memory Manager

  • Use cases: Add/search user-scoped notes, retrieve tagged memories, list recent entries.

  • Inputs: user_id, operation (add/search/list), payload or query.

  • Design tip: Echo back what was stored and how it can be retrieved later.

B) Web Scraper

  • Use cases: Pull and summarize a URL, convert to markdown, extract key facts.

  • Inputs: url, depth (1-hop page is usually enough), output style (summary, outline, Q&A).

  • Design tip: Normalize output: title, canonical URL, headings, bullet summary, citations.

C) Calendar Agent

  • Use cases: Create/modify events; confirm attendees, times, links.

  • Required fields: title, start, end (or duration), timezone.

  • Design tip: If any required field is missing, the agent should ask a single, targeted question.

D) PDF Analyzer

  • Use cases: Answer questions over complex PDFs (tables, images, forms).

  • Inputs: file link or id, question(s), extraction mode (summary, table-to-CSV, cite pages).

  • Design tip: Return page references and confidence notes, not just prose.

5) Routing Patterns That Work

  • Command Phrases: “create event…,” “scrape…,” “analyze PDF…,” “remember…,” “search memories…”

  • Hotkeys (optional): M) Memory, W) Web, C) Calendar, P) PDF. Users can type a single letter to pivot.

  • Disambiguation: If two agents could handle an intent, ask one clarifying question, then commit.

6) UX Details That Reduce Friction

  • Main Menu: Provide a compact list of capabilities and required inputs; render on “menu” or when the user seems lost.

  • Always-Allow Permissions: Configure actions to avoid repeated confirm/deny prompts so the flow stays conversational.

  • Voice Parity: Ensure the same routing works hands-free; keep follow-ups short and specific.

7) Validation, Errors, and Recovery

  • Validate before calling actions. Example: check date formats and timezones.

  • Atomic errors, friendly copy. “Couldn’t create the event: missing end time. Provide an end time or a duration.”

  • Safe fallbacks. If scraping fails, offer a lightweight fetch of metadata or ask for a different URL.

8) Security & Governance (must-haves)

  • Auth hygiene: Centralize key storage; never echo secrets.

  • PII minimization: Only pass what the action needs. Mask emails/IDs in logs where possible.

  • Rate limiting & retries: Back off gracefully; inform the user when limits are hit.

  • Audit trail: Log which agent was called, inputs (redacted), outputs, and latency.

9) Testing & KPIs

  • Golden paths: Script test cases for each agent (happy path and one failure).

  • Metrics to watch: Action success rate, median/95p latency, clarification turns per task, user completion rate, escalation rate.

  • Iteration loop: Review failed calls weekly; refine routing phrases and error prompts.

10) Example Interaction Flow (Condensed)

  1. User: “Schedule a 30-min design sync next Tuesday at 10:00 London time with two attendees.”

  2. GPT (Calendar Agent): “I can do that. Please share attendee emails.”

  3. User: “a@company.com, b@company.com.”

  4. GPT: “Event created. Invite sent. Need reminders or a meeting link?”

  5. User: “Add a video link and a 10-minute reminder.”

  6. GPT: “Done. Anything else?”

11) When to Add More Agents

  • A task repeats often but needs external data (CRM lookups, ticketing).

  • Users paste the same instructions repeatedly (templateable → agent).

  • You need durable state beyond ChatGPT’s native memory (inventory, policies, user profiles).

Quick Checklist

  • Clear capabilities summary and minimal routing rules

  • One schema per agent, with consistent inputs/outputs

  • Required-field prompts and graceful errors

  • Permission settings tuned for “always allow” where appropriate

  • Observability: logs, latency, and success metrics

  • Security: key vaulting, redaction, least-privilege scopes

Multi-agent Custom GPTs turn a chat into an operating surface. With crisp routing, consistent schemas, and thoughtful UX, you can ship assistants that feel cohesive to users while quietly coordinating multiple expert “agents” behind the scenes.

Custom GPTFrancesca Tabor