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Giving AI raw access to enterprise APIs is risky. A single misconfigured prompt could let a model delete records, modify permissions, or query data it shouldn’t see. Specialized agents solve this by wrapping each enterprise system in a governed, scoped agent. Each one is pre-built for a specific system (Salesforce, Jira, Snowflake, GitHub, and many more), restricted to only the operations you approve, and fully observable. You authenticate once, the agent auto-configures, and it’s ready to use. When to use: You want governed AI access to enterprise systems without building anything custom. If the predefined agents don’t cover your use case, use Agent Studio.

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Why not just use a raw connector?

A raw connector gives AI full API access, every endpoint, including destructive operations. A specialized agent wraps that access in governance.
Raw connectorSpecialized agent
AccessFull API surfaceScoped to approved operations only
ConstraintsPrompt-based (model can ignore)Structural (operations not in tool surface)
ObservabilityRaw API logsIntent → plan → execution → outcome per action
DebuggingTrace global reasoningIsolate one bounded agent
CostModel explores all options at runtimePre-structured tool chains, step limits, prompt caching
Example: A GitHub agent for PR triage can read PRs, add labels, and post comments, but it structurally cannot modify org settings, create repos, or touch secrets. Those operations don’t exist in its tool surface.

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How it works

Setting up a specialized agent takes minutes, not days:
  1. Authenticate with OAuth, API key, or service account (one-time)
  2. Auto-configure as the agent discovers schemas, objects, and capabilities, then constrains its operation surface to approved operations only
  3. Route automatically as users ask their Personal AI Agent, and the right specialist handles it
  4. Improve over time as the agent learns your company’s patterns and terminology
Specialized Agent delegation flow

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Observability

When something goes wrong with an AI agent, raw API logs tell you what calls were made but not why. Specialized agents log every action with four fields that capture the full reasoning chain:
FieldWhat it captures
IntentWhat was asked
PlanHow the agent decided to respond
ExecutionWhat it did
OutcomeWhat changed
For example, if a Salesforce agent returns unexpected results, you can see that it intended to query open deals, planned to use the pipeline endpoint, executed a filtered query, and returned 3 results instead of the expected 12. The gap between plan and outcome tells you exactly where to look.

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Available agents

xpander includes specialized agents for Salesforce, Jira, GitHub, Snowflake, BigQuery, Redshift, Datadog, Google Workspace, HubSpot, Slack, and many more. Each one is pre-configured for its system and ready to connect in minutes. The full catalog covers data and analytics, development, project management, productivity, monitoring, CRM, and communication. When you need something beyond the pre-built set, like combining multiple systems, custom escalation policies, or internal APIs, you can build custom agents in Agent Studio.

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What’s next

Agent Studio

Build custom agents when the predefined set isn’t enough.

Tools & Connectors

Configure authentication and connector settings.