Arize observes what agents do. WorkingAgents controls what agents are allowed to do. One without the other leaves a gap: observability without enforcement is monitoring after the fact; governance without observability is controlling what you can’t see. Together, they close the loop.
The Fit
Arize AI ($70M Series C, customers include Uber, PepsiCo, Tripadvisor) is the observability and evaluation layer for AI agents and LLM applications. OpenTelemetry-based tracing across every step — prompts, tool calls, memory, routing, outputs — with LLM-as-a-Judge evaluations for accuracy, tool-calling quality, and goal achievement. Framework-agnostic: OpenAI Agents SDK, LangGraph, CrewAI, LlamaIndex, and more.
WorkingAgents is the access control and orchestration layer. Three gateways (AI, Agent, MCP) govern which tools agents can access, enforce permission boundaries per role, run guardrails at every checkpoint, and log every action with cost attribution.
Arize answers: what did the agent do, and how well did it do it? WorkingAgents answers: was the agent allowed to do it, and who authorized it?
Synergy Areas
1. Observability-Informed Guardrails
Arize’s evaluation data reveals patterns — an agent hallucinating on a specific tool, accuracy dropping for a particular query type, tool-calling failures spiking after a model update. WorkingAgents can act on those signals:
- Arize detects output quality degradation → WorkingAgents tightens guardrails or routes to a different model
- Arize flags a tool-calling failure pattern → WorkingAgents adds a human-in-the-loop checkpoint for that tool
- Arize evaluation scores drop below threshold → WorkingAgents restricts the agent’s scope until the issue is resolved
Observability feeds governance. Governance responds to observability. The system self-corrects.
2. Unified Trace + Audit Trail
Arize traces the how — prompt chains, token usage, latency, model behavior. WorkingAgents logs the who and why — permissions checked, guardrails evaluated, cost attributed, approval workflows triggered.
Merge the two and enterprises get a single view: this agent, operating under these permissions, called this tool, with this prompt chain, producing this output quality score, at this cost, approved by this user. One trace from intent to outcome to evaluation.
3. Evaluation-Driven Access Control
Static permissions don’t account for agent performance. An agent with CRM access that consistently produces high-quality outputs (per Arize evaluation) earns broader scope. An agent whose accuracy degrades gets restricted.
WorkingAgents’ Virtual MCP Servers can adjust permission boundaries based on Arize’s continuous evaluation scores — dynamic access control that responds to observed agent quality, not just predefined roles.
4. Production Feedback Loop for Agent Improvement
Arize’s strength is the dev-to-production improvement cycle. WorkingAgents’ strength is governing what happens in production. Combined:
- Arize evaluates agent performance in production
- Insights identify where agents underperform
- Improved agents are deployed through WorkingAgents’ gateway
- WorkingAgents’ permission model gates the rollout (canary deployment — new model serves 10% of traffic, Arize evaluates, WorkingAgents expands if quality holds)
Governed, observable, continuous improvement.
Starting Point
WorkingAgents’ audit logs (tool calls, permissions, guardrails, costs) can be exported as OpenTelemetry spans — the format Arize already ingests. The integration path is a telemetry bridge, not a platform rewrite. Arize gets richer traces that include governance context. WorkingAgents gets evaluation signals that inform dynamic guardrails.
One conversation to explore: a joint demo showing an agent operating through WorkingAgents’ MCP Gateway, traced end-to-end in Arize, with guardrails that respond to evaluation scores in real time.
WorkingAgents is an AI governance platform specializing in agent access control, orchestration, and security for enterprises deploying AI at scale.