As enterprises move from agent experiments to production deployments, a standard agent stack is emerging. Here’s what it looks like.
The Four Layers
1. Model Layer
The foundation. Enterprises are adopting a multi-model strategy:
- Frontier models (Claude, GPT-4) for complex reasoning
- Specialized models for classification, extraction, summarization
- Fine-tuned small models for high-volume, low-latency tasks
2. Agent Framework Layer
The orchestration middleware. LangGraph leads in production, with CrewAI gaining for simpler use cases (see our open-source frameworks comparison). Key requirements:
- State persistence across sessions
- Human-in-the-loop intervention points
- Audit logging for compliance
- Rate limiting and cost controls
3. Tool Integration Layer
Connecting agents to enterprise systems:
- MCP servers for standardized tool access (learn more about MCP in our complete guide to AI agents)
- Internal API gateways with auth and throttling
- Database connectors (read-only agents, write-audited agents)
- File system agents with strict access controls
4. Observability Layer
You can’t run agents without visibility:
- Tracing — Every agent step, tool call, and decision logged
- Cost tracking — Per-agent, per-user, per-task cost attribution
- Quality scoring — Automated evaluation of agent outputs
- Alerting — Anomaly detection for unusual agent behavior
Production Patterns
Pattern 1: Guarded Agent — Agent + guardrails + human approval for critical actions
Pattern 2: Agent Pipeline — Serial agent chain: Extract → Analyze → Generate → Review
Pattern 3: Agent Swarm — Parallel specialized agents with an orchestrator
The Bottom Line
Enterprise agents are no longer a question of if but how. The stack is converging, the tools are maturing, and the ROI cases are clear. The winning architectures will be those that balance autonomy with control — giving agents enough freedom to be useful while maintaining enough oversight to be safe.