Building Production-Ready AI Agent Systems: Multi-Agent Orchestration and LLMOps at Scale

galileo_/_crew_ai · Tech · 2025 · source Agentic: yes · promotional bias: high · source-backed · extracted with prompt 0.5.5 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Enterprise-scale multi-agent orchestration, observability, governance, and LLMOps

Galileo and CrewAI discuss what enterprises need to deploy many agentic workflows in production: an agent stack with orchestration, memory, authentication, authorization, connectors, protocols, observability, custom evaluators, CI/CD integration, governance, and a control plane that scales from prototypes to hundreds or thousands of agents.

Constraints:

• Agent systems are non-deterministic, so traditional deterministic tests and traces are insufficient.
• Enterprise agents need authentication and dynamically changing authorization scopes while they execute.
• Production multi-agent systems introduce many more failure modes than simpler RAG or chatbot systems.
• Governed industries need auditability, traceability, policy enforcement, and custom evaluation metrics tied to business rules.
• Organizations may deploy many agent use cases per month, creating control-plane complexity around monitoring, ownership, and regression prevention.
• Protocol fragmentation across MCP, A2A-like efforts, cloud providers, and vendor agents threatens interoperability.

Architecture

The discussed architecture is an enterprise agent stack. It starts with existing data platforms such as Databricks, Snowflake, and BigQuery, then adds LLM orchestration, memory, authentication, authorization, connectors, tool/agent protocols, CrewAI-style agent orchestration, Galileo-style observability/evaluation, custom SLM/LLM evaluators, policy evaluators, CI/CD gates, insights engines, and agentic applications with natural-language UIs. At scale, a control plane monitors agent executions, tool reliability, hallucination patterns, best/worst agents, and cross-crew optimizations. No-code and repositories democratize creation while enterprise tooling handles governance, measurement, and reliability.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  enterprise-data-platforms[Enterprise data platforms] --> agent-orchestration-layer[Agent orchestration layer]
  agent-orchestration-layer --> memory-layer[Memory layer]
  agent-orchestration-layer --> authentication-and-authorization[Authentication and authorization]
  agent-orchestration-layer --> connectors-and-tools[Connectors and tools]
  agent-orchestration-layer --> agent-communication-protocols[Agent communication protocols]
  agent-communication-protocols --> external-vendor-agents[External vendor agents]
  agent-orchestration-layer --> agentic-applications[Agentic applications]
  connectors-and-tools --> observability-and-evaluation-layer[Observability and evaluation layer]
  observability-and-evaluation-layer --> custom-evaluator-pipeline[Custom evaluator pipeline]
  custom-evaluator-pipeline --> governance-policies[Governance policies]
  custom-evaluator-pipeline --> ci-cd-gate[CI/CD gate]
  observability-and-evaluation-layer --> insights-engine[Insights engine]
  ci-cd-gate --> control-plane[Control plane]
  insights-engine --> control-plane[Control plane]
  agentic-applications --> control-plane[Control plane]
  control-plane --> no-code-agent-builder[No-code agent builder]
  no-code-agent-builder --> agent-repository[Agent repository]
  agent-repository --> agent-orchestration-layer

Components

Data flow

Enterprise data remains in platforms such as Databricks, Snowflake, and BigQuery. Agent builders compose crews using orchestration, memory, connectors, tools, authentication, and authorization. Agents execute workflows, call tools, hand off work, and produce actions or outputs. Observability captures traces and higher-level agent metrics, while custom evaluators encode business policies and score quality, hallucination, tool correctness, action completion, and governance requirements. Evaluators are baked into SDLC and CI/CD so changes can be checked before deployment. Insights engines aggregate across crews to identify recurring failures and optimization opportunities. No-code and repositories broaden access, but enterprise control-plane features are needed once agent counts scale.

Why this architecture

The stack exists because agent production is broader than prompt/model/context tracing. Enterprises need provisioning, auth, authorization scopes, protocol interoperability, observability, custom business evaluators, CI/CD gates, governance, and control-plane visibility once they run many crews or agents in regulated environments.

Models used: CrewAI · Galileo · Luna models · Custom SLM evaluators · LLM-as-a-judge · Databricks · Snowflake · BigQuery · MCP · A2A · Bedrock agents · Azure AI Studio

Scale

• One partner context involved about 900 people and $20 million deals.
  “This company is huge, right? It was like a 900 people in there”transcript, stated

  “these guys are closing this insane deals like $20 million deals”transcript, stated
• The enterprise target state discussed is hundreds or thousands of production agents.
  “I'm going to have hundreds or maybe thousands of agents running production in x years time”transcript, stated
• The discussion mentions potential physical-server savings of 60% at huge scale.
  “if you're running this at a huge scale and you can save 60% by running this like like in a physical server”transcript, stated
• A top-10 US bank had already built a multi-agent system for its use cases.
  “top 10 bank in the United States and they already built a multi-identicing system”transcript, stated
• The speakers discuss adoption of 100 crews across an enterprise.
  “with the adoption of 100 crews across an enterprise”transcript, stated
• Advanced customers are putting about five use cases into production per month.
  “some of like our most advanced and like customers now they're putting around five use cases in production a month.”transcript, stated
• The discussion describes a reusable repository of 50 agents.
  “If you can build to Vicron's point 50 amazing agents and you can put them in a repository”transcript, stated
• Enterprise open-source scaling is framed around running a thousand agents.
  “imagine what running a thousand agents on open source going to look like and like how you going to control that”transcript, stated
• Reliability discussions anticipate 100 agents talking to each other and millions of tasks.
  “you're going to have a 100 different agents uh talking to each other.”transcript, stated

  “You're going to have millions of different uh uh tasks that need to be completed all the time”transcript, stated

Alternatives considered (roads not taken)

Traditional LLM tracing only

Rejected because: The speakers say tracing connectors around calls falls short for agents because there are many additional layers to observe and evaluate.

Generic magic metrics such as faithfulness

Rejected because: The Galileo speaker argues that agent reliability must be measured with business-specific custom evaluators rather than fixed generic metrics.

Cloud-provider-owned proprietary protocol control

Rejected because: The speakers argue open interoperability is needed and that protocol ownership as a wedge would fragment the ecosystem.

Open source alone for enterprise-scale operations

Rejected because: CrewAI frames open source and enterprise as complementary, with enterprise needed for control-plane concerns at large scale.

Restrict agent building to technical teams

Rejected because: CrewAI argues democratized no-code access is needed to transform companies and enable broad reuse of agent repositories.

Patterns

multi-agent-orchestration (agent-platform)

CrewAI is discussed as the orchestration/control-plane layer for building crews, scaling use cases, and managing multi-agent workflows.

observability-tracing (operations)

The stack monitors executions, best/worst agents, hallucination patterns, broken tools, handoffs, action completion, and cross-crew optimization opportunities.

llm-as-judge (evaluation)

LLM-as-judge is presented as a useful but incomplete base for measuring quality and hallucination before going custom.

eval-driven-development (llmops)

Custom evaluators and metrics are integrated into SDLC and CI/CD so teams can measure success and catch regressions before deployment.

guardrails-io-filtering (governance)

Risk-management policies are encoded as evaluators and baked into the system so banks can show how agents made decisions.

tool-use-orchestration (execution)

The discussion treats tool choice, missing tools, duplicated tools, handoff quality, and action completion as first-class reliability dimensions.

human-in-the-loop-gate (evaluator-tuning)

Expert human feedback tunes custom evaluation metrics and provides continuous learning for high-risk enterprise deployments.

data-quality-loop (feedback-loop)

The proposed future moves from offline metrics toward automatic feedback loops that detect production failures and improve prompts or tool calls.

Tradeoffs

Use custom evaluators instead of fixed generic metrics

Chose Business-specific high-accuracy evaluators and custom SLMs over Magic metrics such as generic faithfulness.

Rationale: Use cases differ by business process, action type, compliance policy, and success definition, so evaluation must encode those specifics.

Keep open-source and enterprise offerings complementary

Chose Open-source framework plus enterprise control plane over Assuming open source alone is enough at fleet scale.

Rationale: Open source remains useful, but enterprise deployments need control, governance, monitoring, and ownership across hundreds or thousands of agents.

Democratize agent creation through no-code

Chose No-code and reusable agent repositories over Only pro-code agent building.

Rationale: Transforming companies requires more than a subset of technical builders, but no-code must be paired with governance and reusable assets.

Favor open standards for protocols

Chose Cross-industry open-source protocol collaboration over Vendor-owned protocol wedges.

Rationale: Agent marketplaces and cross-company agent interactions need interoperability more than any single provider needs control of the protocol.

Move from metrics engines to insights engines

Chose Automatically surface failure modes and next actions over Asking developers to inspect every metric and discover issues manually.

Rationale: Complex agent fleets create too many signals; developers need synthesized five-to-ten issue lists and eventually automated fixes.

Gotchas (what bit them)

Prototype success hides production failure modes.

Root cause: Agents have non-deterministic behavior and can fail in far more ways than simple RAG systems.

Resolution: Add custom evals, observability, scale testing, CI/CD checks, and insights before scaling use cases.

Authentication is not just identity; dynamic authorization scope matters.

Root cause: Agents may need changing access scopes during execution depending on who runs the workflow and what the task requires.

Resolution: Treat authentication, authorization, and scope changes as dedicated stack layers rather than assuming existing app auth is enough.

Regulated industries cannot rely on generic metrics.

Root cause: Banks need policies, auditability, risk-management rules, and use-case-specific business definitions embedded into evaluation.

Resolution: Build high-accuracy custom evaluators and feed them into SDLC/CI/CD and governance processes.

Protocol fragmentation can block multi-agent interoperability.

Root cause: Cloud providers and vendors may try to own protocol layers, while enterprises need agents from many providers to work together.

Resolution: Favor open-source, cross-industry standards for tool calling and agent communication.

Scaling use cases creates ownership and monitoring overload.

Root cause: Five new production use cases per month quickly accumulates into many crews and agents without clear monitoring ownership.

Resolution: Use control-plane features to monitor agent fleets, broken tools, hallucination patterns, and operational health across all crews.

Non-technical no-code users may still be afraid of breaking systems.

Root cause: Democratized tooling alone does not create confidence if users are unsure what actions are safe.

Resolution: Pair no-code access with reusable repositories, governance, and clear control-plane protections.

Deploying a change can break behavior that previously worked.

Root cause: Agent behavior shifts with prompts, tools, and model behavior, and teams often lack measurements before first production deployments.

Resolution: Run regression prompt sets and automated evals before deploy, then build production feedback loops for new failure modes.

Evaluation

The discussion advocates custom evaluator-driven AgentOps. Teams should define what success looks like before building, then implement measurements for quality, hallucination, action completion, business impact, policy compliance, tool/handoff quality, regressions, and fleet-level patterns. LLM-as-judge can help, but high-risk enterprise deployments need custom SLM/LLM evaluators, human expert feedback, CI/CD gates, insights engines, and production feedback loops.

Metrics: Business-specific success definition · Quality and hallucination · Action completion · Bottom-line impact · Tool called/not-called correctness · Handoff quality · Broken-tool rate · Best/worst agents · Hallucination by agent · Policy compliance · CI/CD regression prompts · Top five-to-ten failures or optimizations · Production feedback-loop failure modes

Outcomes

• CrewAI is positioned as a multi-agent orchestration/control-plane platform with open-source and enterprise components.
• Galileo is positioned as the evaluation, observability, reliability, custom evaluator, and insights layer for agentic systems.
• Financial services and insurance are described as adopting agents quickly despite regulation.
• A top-10 US bank reportedly built a multi-agent system and asked how to bake governance policies into it.
• Advanced customers are reportedly putting about five use cases into production per month.
• The speakers predict a move from magic metrics to insights engines and automatic feedback loops for prompt/tool-call improvement.
• No-code and repositories are framed as the path to democratizing agent creation across enterprises.

The discussion is vendor-led and promotional, but it includes concrete scale claims: 900-person partner offsite, $20M deals, hundreds/thousands of production agents, 60% physical-server savings possibility, top-10 US bank adoption, 100 crews across an enterprise, five production use cases per month, 50 reusable agents, a thousand-agent open-source control question, 100 agents talking to each other, and millions of tasks.