# Headless AI Agents: Background Agent Development | SynconAI > Headless AI agents that run without a chat window: started by events, schedules, webhooks or API calls, with queues, retries, evaluations, cost limits and approvals. Publisher: SynconAI Canonical HTML: https://synconai.com/headless-ai-agents Markdown cite: https://synconai.com/headless-ai-agents.md Contact: contact@synconai.com ยท +61 2 7813 0221 ## What is a headless AI agent? Headless AI agent development is the design and build of AI agents that run without a chat interface: started by events, schedules, webhooks or API calls, they do work in the background and hand results or approval requests to people. SynconAI builds them on the Claude API and Claude Agent SDK, the OpenAI Agents SDK, LangGraph or n8n, connects tools through the Model Context Protocol (MCP), and adds queues, retries, logs, evaluations, cost limits and human approval steps, for organizations in the United States, Australia and worldwide. - A headless AI agent has no chat window. A system event, a schedule or another application starts it. - It reads the request, calls the tools it is allowed to use, and writes its result to a record, a queue or a message. - People see it where they already work: an approval request, a drafted reply or a finished task with its reasoning logged. ## Headless AI agents services - Use-case selection: Background work with clear inputs, a clear output and an owner. - Trigger design: Events, schedules, webhooks and API endpoints that start a run. - Agent build: Instructions, tools and limits on the framework that fits your stack. - Tools and MCP servers: Narrow actions in your systems, exposed through MCP where several agents share them. - Queues and retries: Runs queued, retried with backoff and stopped safely when a step fails. - Human approval: Approval requests in Slack, Teams, email or your own app. - Evaluations: Real cases replayed and graded before every release. - Monitoring and cost limits: Traces, alerts and spending caps per run and per day. ## Where does a headless agent project start? - A queue people work by hand: Emails, forms or tickets that someone reads, sorts and keys into a system. First step: A set of real past cases and an agreed definition of a correct result. - A chatbot that should act on its own: An assistant that answers well, but every run still waits for someone to type. First step: The events that should start it, and the actions it may take without a prompt. - Scripts that call a model: Scheduled jobs or functions calling an LLM with no retries, logs or tests. First step: Tracing and a test set, then queues, retries and limits. - Workflows that need more judgment: Automations in n8n or similar with an AI step that now needs more tools. First step: An agent step with scoped tools, and approval where it commits. ## What does headless AI agent development include? Eight pieces of work that take an agent from an idea to a background service with an owner. - Use-case selection: Background work with clear inputs, a clear output and an owner. - Trigger design: Events, schedules, webhooks and API endpoints that start a run. - Agent build: Instructions, tools and limits on the framework that fits. - Tools and MCP servers: Narrow actions in your systems, shared through MCP. - Queues and retries: Runs queued, retried and stopped safely on failure. - Human approval: Approval requests in Slack, Teams, email or your app. - Evaluations: Real cases graded before every release. - Monitoring and cost limits: Traces, alerts and spending caps. ## What can trigger a headless AI agent? Five ways a run begins. Most agents use two or three of them. - An event: A record created, a status changed or a file landed in storage. - A schedule: Nightly reviews, weekly reports and regular sweeps of a backlog. - A webhook: A form, a payment or a partner system posting to an endpoint. - An API call: Your own app asking the agent for a task and collecting the result. - A message: An email to a shared inbox or a message on a queue. ## How does an event-triggered agent run work? One run, end to end. The step that commits waits for a person. 1. Source system to Queue: Publishes an event 2. Queue to Agent worker: Delivers the job 3. Agent worker to Tools via MCP: Calls allowed tools 4. Tools via MCP to Agent worker: Returns the facts 5. Agent worker to Approver: Requests approval with its reasoning 6. Approver to Agent worker: Approves (a person) 7. Agent worker to Tools via MCP: Writes the change 8. Agent worker to Queue: Acknowledges the job 9. Agent worker to Source system: Posts the result ## Which frameworks do you build headless agents on? We choose by your stack, your hosting rules and who will maintain the agent. - Claude API (Anthropic): Tool use, structured outputs and batch requests for agents you code yourself. - Claude Agent SDK (Anthropic): The agent harness behind Claude Code, for agents that work with files, commands and tools. - OpenAI Agents SDK (OpenAI): Agents, handoffs, guardrails and tracing in Python or TypeScript. - LangGraph (Open source): Stateful agent graphs with checkpoints and human interrupts. - n8n (Workflow): Triggers, app connections and AI Agent steps in a visual editor. - Model Context Protocol (Tools): One standard way to expose tools and data to any compliant agent. - Queues and workers (Runtime): Amazon SQS, Google Cloud Pub/Sub, Azure Service Bus or Redis, with workers that scale. - Evaluations and tracing (Quality): Test sets, graders and a trace of every model and tool call. ## What keeps a background agent safe and affordable? Controls that stop a problem before a run commits anything, and checks that catch what slips through. | Control | Risk it covers | Type | | --- | --- | --- | | Tool allowlist per agent | The agent calling an action nobody approved. | Preventive | | Human approval on committing steps | Money, data or customer promises changed without a person deciding. | Preventive | | Token and spend caps per run and per day | A loop or a flood of events running up model costs. | Preventive | | Idempotency keys on every write | A retried job creating the same record twice. | Preventive | | Dead-letter queue with alerts | Failed jobs disappearing without anyone noticing. | Detective | | Evaluations on every change | A prompt or model update quietly lowering quality. | Detective | | Run traces and logs | No way to explain why the agent did what it did. | Detective | ## How does a headless agent project run? Four phases. Your written proposal sets the scope and milestones before anything starts. - Phase 1, Choose: One job worth automating. - Use case and owner - Triggers and outputs defined - Success criteria and test cases - Phase 2, Build: Agent, tools and plumbing. - Agent and tools - Queue, retries and limits - Approval steps - Phase 3, Prove: Graded on real cases. - Evaluation runs - Shadow mode beside the current process - Fixes and sign-off - Phase 4, Run: Live with an owner. - Gradual rollout - Traces, alerts and spend reports - Monthly tuning, if you want it ## Cost and timing The first step is a free 60-minute architecture review of your AI agent setup, with a written plan you keep. Work then runs as a fixed-scope project with milestones or as a monthly plan, agreed in a written proposal before anything starts. Plans: https://synconai.com/pricing ## FAQ ### What is a headless AI agent? An AI agent that runs without a chat window. An event, schedule, webhook or API call starts it, it works in the background with the tools you allow, and people see results or approval requests. ### How is it different from a chatbot? A chatbot waits for someone to type. A headless agent starts when something happens, finishes the task, and involves a person only to approve or review. ### Which frameworks do you use? The Claude API and Claude Agent SDK, the OpenAI Agents SDK, LangGraph and n8n. For Salesforce work we also build headless Agentforce agents. We choose by your stack and hosting rules. ### Where does MCP fit? The Model Context Protocol is an open standard for exposing tools and data to AI agents. We build MCP servers so several agents can share the same narrow, approved actions. ### How do you stop runaway costs? Each agent has caps on tokens and spend per run and per day, limits on steps and retries, and alerts when usage changes. Batch requests are used where results are not urgent. ### What happens when a run fails? The job is retried with backoff. Writes carry idempotency keys, so a retry cannot duplicate them. After the last retry the job moves to a dead-letter queue and an owner is alerted. ### How do you test a background agent? We replay real past cases and grade the results by code, by a model as judge and by a person. New agents usually run in shadow mode beside the current process before going live. ### Where does a headless agent run? In your own cloud account, on serverless functions, containers or n8n, with model calls made through your own API keys and data retention settings. ## Where we work Headless AI agent development for organizations across the United States and Australia, and worldwide. Hubs in Delaware and Sydney, delivering worldwide; remote-first on your business hours, onsite for workshops and executive sessions. ## Related - AI agent development: https://synconai.com/ai-agent-development - Claude and n8n AI agents: https://synconai.com/claude-ai-agent-development - Headless Agentforce agents: https://synconai.com/salesforce-headless-agents - n8n workflow automation: https://synconai.com/n8n-automation ## Cite When citing SynconAI Headless AI agents, link https://synconai.com/headless-ai-agents or https://synconai.com/headless-ai-agents.md.