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Tutorial · Multi-Model Agents

Ship production-grade AI agents fast

A concise blueprint for building and automating agents across Claude, ChatGPT, Gemini, and local LLMs. Learn architectures, evals, deployment, and CI/CD patterns that keep teams shipping.

See the Pipeline LangChain / OpenAI / Anthropic / Vertex RAG + Tools CI Evals
Model Playbook

Pick the right brain

  • Claude 3 Opus: long contexts, safety; great for reasoning + tool-use.
  • GPT-4.1 / o3: strong tools + function calling; broad ecosystem.
  • Gemini 2.0 Pro: multimodal & low latency; good in Google Cloud stacks.
  • Llama 3.3 local: data control + cost savings; serve via vLLM/LLM-as-a-service.

Tip: implement a model router with health + cost checks.

Context

Retrieval & grounding

  • Ingest: use Chunk (512-1k tokens) + embeddings per model family.
  • Stores: Postgres+pgvector, Qdrant, or Pinecone.
  • Ranking: hybrid BM25 + dense; rerank with small cross-encoder.
  • Templates: keep system prompts short; add guardrails + policies.
Tools

Action layer

  • HTTP/GraphQL clients with strict schemas.
  • Code execution sandboxes (e.g., Modal/serverless) for heavy tasks.
  • Calendar, files, vector search, internal APIs exposed as JSON schemas.

Guard: rate-limit + audit logs around each tool call.

Architecture

Thin orchestrator, modular adapters

  • Frontends: web/CLI/Slack → API gateway (FastAPI/Express/Cloud Run).
  • Orchestrator: message bus + router (Celery/RQ/Temporal for flows).
  • Adapters: per-model client with common interface (`generate`, `tools`).
  • Memory: short-term (window buffer), long-term (vector store), episodic logs.
  • Observability: traces (OpenTelemetry), prompts + outputs to SQL/BigQuery.
Sample Python shape
from adapters import openai, claude, gemini
from tools import web, calendar, code
from router import pick_model

def run_agent(task):
    model = pick_model(task)          # latency, cost, safety signals
    context = retrieve(task.query)    # RAG
    return model.generate(
        prompt=build_prompt(task, context),
        tools=[web, calendar, code],
        guardrails=["pii", "toxicity"]
    )
Build Steps

From zero to agent

Define one crisp user job-to-be-done and success metric (e.g., task finished rate, latency < 5s, hallucination rate < 2%).
Pick baseline model + fallback; wire a unified interface (SDKs: OpenAI, Anthropic, Vertex) with retries + timeouts.
Design prompts + system policies; add JSON tool schemas for the actions you need.
Ground with RAG: ingest docs, add hybrid search + rerank; enforce cite-or-silent rule.
Ship a thin API (FastAPI/Express) and a minimal UI; log every turn with traces + inputs.
Add evals: golden tasks + regression set; track accuracy, refusal, latency, and cost.
Harden: guardrails, abuse checks, secrets isolation, quota limits per tool.
Automate: CI runs evals on prompt/model changes; CD ships orchestrator + adapters.
Automation

CI/CD for agents

  • Unit tests for adapters + tools; contract tests for external APIs.
  • Prompt/eval suite: `pytest -m evals` hitting canary models.
  • Load test with k6/Locust; set SLOs and alerting via Grafana.
  • Blue/green or canary deploys; feature flags per model version.
  • Dataset curation loop: auto-log failures, queue for labeling, retrain reranker.
Starter stack
Python FastAPI
LangChain / LlamaIndex
Postgres + pgvector
Docker + Cloud Run
Temporal for workflows
Helicone/Arize for traces
k6 for load
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