Expert Consultation: Building Production-Grade AI Agents using OpenAI Agent Builder & Gemini Agent Builder

We are looking for a senior-level AI Agent Architect / LLM Engineer to conduct a deep-dive consultation session on designing, building, and deploying production-grade AI agents using:

OpenAI Agent Builder (GPT-based agents)

Google Gemini Agent Builder / Vertex AI Agents

This is not a beginner or tutorial-style engagement. The objective is to gain expert-level clarity on agent architecture, tool orchestration, connectors, memory design, evaluation, and real-world deployment patterns.

Scope of Consultation (Core Topics)

The session should cover, in depth:

1. Agent Architecture & Design

Single-agent vs multi-agent systems

Planner-executor-reflector patterns

Deterministic vs probabilistic agent flows

When agents outperform workflows (and when they don’t)

2. OpenAI Agent Builder – Deep Dive

Agent Builder internals and limitations

Tool calling and function schemas (advanced patterns)

File, browser, code, retrieval and custom tool integration

Prompt layering: system → developer → agent memory

Guardrails, refusal handling, and safety controls

Cost optimization and latency trade-offs

3. Gemini Agent Builder / Vertex AI Agents

Gemini agent architecture vs OpenAI agents

Native connectors (BigQuery, GCS, Google Drive, APIs)

Tool invocation and grounding with enterprise data

Differences in memory, reasoning, and orchestration

Strengths/weaknesses vs OpenAI agents

4. Connectors & Data Integration (Critical)

Designing scalable connectors (APIs, databases, SaaS tools)

Retrieval-augmented agents vs tool-based agents

Sync vs async data flows

Security, permissions, and access control

Handling rate limits, failures, and retries

5. Memory Systems

Short-term vs long-term memory

Vector stores vs structured memory

Memory decay, summarization, and replay

Session memory vs persistent memory

6. Evaluation, Reliability & Monitoring

Agent evaluation frameworks

Hallucination control techniques

Logging, tracing, and observability

Regression testing for agents

Human-in-the-loop patterns

7. Production & Deployment

When to use hosted agent builders vs custom frameworks

Scaling agents for real users

Cost governance and budgeting

Versioning agents and prompts

Compliance and data privacy considerations