# Headless Agent Platform

Integrate AI agents into your systems through APIs. Centralize governance. Keep execution within your security perimeter.

[← Back to use cases](/use-cases/)

## The Problem

Enterprises are building AI agents at scale. [82% of enterprises](https://www.aicerts.ai/news/agent-management-microsoft-tackles-enterprise-agent-sprawl/) use AI agents daily, with IDC forecasting 1.3 billion enterprise agents by 2028.

But integrating these agents into internal systems creates challenges:

- **Agent sprawl** — 44% of enterprises cite technical debt from uncontrolled agent proliferation
- **Governance gaps** — 80% of organizations have encountered risky behaviors from AI agents, including improper data exposure
- **Security concerns** — 78% of CIOs cite security and compliance as primary barriers to scaling agent-based AI
- **Integration complexity** — Each agent needs connections to internal tools, databases, and services

Traditional agent interfaces are designed for human interaction. When agents need to work as backend services - processing data, triggering automations, connecting systems - you need headless execution.

## What is Headless Agent Execution?

A [headless AI agent](https://useinsider.com/glossary/headless-ai-agent/) runs entirely in the background through APIs, with no user interface. It processes data, makes decisions, and performs tasks through backend automation.

This enables:

- **System integration** — Agents called from microservices, middleware, and orchestration layers
- **Multi-agent coordination** — Agents communicating with each other programmatically
- **Workflow embedding** — AI decision-making inside existing automation pipelines
- **Backend automation** — Complex processes without human intervention

Salesforce's [Agent API](https://developer.salesforce.com/blogs/2025/04/build-headless-agents-with-the-agent-api) exemplifies this approach: agents running server-side, callable from any system, without relying on Salesforce's UI.

## Headless Agent SDKs

Major AI platforms now offer SDKs for headless agent execution:

- **[GitHub Copilot SDK](https://github.com/github/copilot-sdk)** — Embed Copilot's agentic capabilities into applications. Available for Python, TypeScript, Go, and .NET.
- **[Claude Agent SDK](https://github.com/anthropics/claude-agent-sdk-python)** — Programmatic access to Claude Code's capabilities. Build agents that read files, run commands, and edit code.
- **[Cloudflare Agents SDK](https://developers.cloudflare.com/agents/)** — Deploy agents on edge infrastructure with state persistence.

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">The GitHub Copilot SDK is now available for developers to build agentic AI workflows into any application. Same Copilot core, now embeddable everywhere.</p>&mdash; Satya Nadella (@satyanadella) <a href="https://x.com/satyanadella/status/2014360953111060894">January 22, 2026</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

This convergence signals that headless execution is becoming the standard interface for AI agents in production systems.

## How Everruns Helps

Everruns provides the execution infrastructure for headless enterprise agents:

1. **Durable execution** — Managed event loop composed from atoms. Every step persisted. Agents resume from where they left off after failures.
2. **On-premise / VPC deployment** — Run within your security perimeter. Sensitive data never leaves your infrastructure.
3. **API-first design** — Agents exposed as services. Call them from any system.
4. **Centralized orchestration** — Single platform for all agent workloads.

## Governance-Ready Infrastructure

Headless agents running critical business processes need governance:

- **Audit trails** — Full logging of agent actions for compliance
- **Scoped execution** — Agents run with minimal required permissions
- **Kill switches** — Halt agents immediately if needed
- **Real-time visibility** — Streaming events show what agents are doing

[MCP gateways](https://airia.com/airia-launches-mcp-gateway/) handle tool access control. Control planes like Agent 365 handle agent management. Everruns handles the execution layer - ensuring agents complete their work even when infrastructure fails.

## Security Perimeter

Traditional perimeter security has shifted. Data flows through browsers, SaaS tools, and AI interfaces. For sensitive workloads, on-premise deployment keeps AI execution within your infrastructure.

Everruns supports deployment inside your security perimeter:

- VPC or on-premise installation
- No data leaves your infrastructure
- Integration with existing identity and access management
- Compliance with data sovereignty requirements

## Use Case Examples

- **Backend automation** — Agents processing orders, updating inventory, coordinating logistics
- **Data pipeline integration** — AI enrichment steps in ETL workflows
- **Cross-system orchestration** — Agents coordinating actions across Slack, Jira, GitHub, internal tools
- **Compliance workflows** — Automated document review, policy checking, audit preparation
- **Customer operations** — Headless agents handling refunds, account updates, support escalation

## Further Reading

- [Build an agent into any app with the GitHub Copilot SDK](https://github.blog/news-insights/company-news/build-an-agent-into-any-app-with-the-github-copilot-sdk/) — GitHub's approach to programmatic agent execution
- [Building agents with the Claude Agent SDK](https://www.anthropic.com/engineering/building-agents-with-the-claude-agent-sdk) — Anthropic's framework for headless agents
- [Build Headless Agents with the Agent API](https://developer.salesforce.com/blogs/2025/04/build-headless-agents-with-the-agent-api) — Salesforce's approach to headless agents
- [Microsoft Agent 365: The control plane for AI agents](https://www.microsoft.com/en-us/microsoft-365/blog/2025/11/18/microsoft-agent-365-the-control-plane-for-ai-agents/) — Enterprise governance for AI agents
- [Securing the Model Context Protocol (MCP)](https://arxiv.org/abs/2511.20920) — Security considerations for agent tool access
- [Agentic AI security: Risks & governance](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/deploying-agentic-ai-with-safety-and-security-a-playbook-for-technology-leaders) — McKinsey playbook for enterprise deployment