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Start with your question

Each path walks through the decisions in the order teams usually face them, with the category to compare at every step. Pick the one closest to what you are trying to do.

Roll out Copilot, ChatGPT or Claude to staff safely

Pick the assistant, then put data protection, ownership and access rules around it before the first wave of users.

  1. Assistants & copilotsUse

    Which suite fits where people already work, and which plan gives the admin controls, audit logs and data residency you need.

    For example: GitHub Copilot, Cursor, Databricks Genie Agents

  2. Guardrails & securityControl plane

    How sensitive data is labelled and kept out of prompts and answers, and who reviews blocked or flagged activity.

    For example: NeMo Guardrails, Guardrails AI, Lakera Guard

  3. Registry & managementControl plane

    Where agents that users build inside the assistant get registered, who owns each one, and how they are retired.

    For example: Paperclip, Amazon Bedrock AgentCore, IBM watsonx Orchestrate

  4. Identity & accessControl plane

    Whether those agents act as the user or under their own identity, and how their access is reviewed.

    For example: Okta for AI Agents, Saviynt Zuma, Google Agent Identity

Put our first agent into production

Build it with a framework your team can support, wire it into the process, then route, watch and guard it before go-live.

  1. Agent frameworksBuild & orchestrate

    A managed cloud agent service or an open source framework, judged on deployment surface, license risk and the skills you have.

    For example: Microsoft Agent Framework, OpenAI Agents SDK, Mastra

  2. Workflows & orchestrationBuild & orchestrate

    Which steps stay deterministic, where humans approve, and which workflow engine runs them.

    For example: LangFlow, n8n, Haystack

  3. AI & MCP gatewaysControl plane

    One route for model and tool calls, with keys, budgets and rate limits per team or agent.

    For example: agentgateway, Azure API Management AI gateway, Cloudflare AI Gateway

  4. Monitoring & evaluationControl plane

    How you trace each run, test quality before release, and catch cost or quality drift after it.

    For example: MLflow, Datadog Agent Observability, Arize Phoenix

  5. Guardrails & securityControl plane

    Which prompts, outputs and actions are blocked at runtime, and how incidents are escalated.

    For example: NeMo Guardrails, Guardrails AI, Lakera Guard

Get control of the agents we already have

Find every agent first, give each one an owner and identity, then apply policy and monitoring across the fleet.

  1. Registry & managementControl plane

    How agents from every platform are discovered, registered and assigned an accountable owner.

    For example: Paperclip, Amazon Bedrock AgentCore, IBM watsonx Orchestrate

  2. Identity & accessControl plane

    Least-privilege access per agent, with access reviews and a way to switch one off quickly.

    For example: Okta for AI Agents, Saviynt Zuma, Google Agent Identity

  3. Guardrails & securityControl plane

    Runtime guardrails and posture checks that apply no matter which tool built the agent.

    For example: NeMo Guardrails, Guardrails AI, Lakera Guard

  4. Monitoring & evaluationControl plane

    Fleet-wide visibility into what agents did, what it cost and whether outcomes are improving.

    For example: MLflow, Datadog Agent Observability, Arize Phoenix

Decide whether to allow always-on agents

These agents act without a prompt each time, so treat the decision like granting a new employee access, not like installing an app.

  1. Always-on agentsUse

    Where the agent runs, what it can reach, which actions need approval, and its security record.

    For example: Microsoft Autopilot, Muse, Grok Bot

  2. Identity & accessControl plane

    Whether it gets its own identity with scoped, revocable access instead of a person's credentials.

    For example: Okta for AI Agents, Saviynt Zuma, Google Agent Identity

  3. Guardrails & securityControl plane

    How it is sandboxed and how third-party skills or plugins are vetted before use.

    For example: NeMo Guardrails, Guardrails AI, Lakera Guard

  4. Registry & managementControl plane

    How these agents are inventoried alongside the rest of the fleet, with an owner for each.

    For example: Paperclip, Amazon Bedrock AgentCore, IBM watsonx Orchestrate

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