AI Agent Deployment Platforms in 2026: The Runtime Landscape
A survey of the AI agent platform landscape in 2026 — four archetypes, three structural trade-offs, and the questions that matter when choosing where to run your agents.
12 min read
Deploy AI Agents
at Enterprise Scale
Stop building infrastructure. Start shipping agents. Connic handles deployment, scaling, and integrations so you can focus on what matters: building intelligent systems.
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agents/support-agent.yaml
1version: "1.0"23name: support-agent4model: gemini-2.5-pro5description: "Customer support AI"67system_prompt: | 8 You are a helpful support agent.9 Use tools to look up orders and10 resolve customer issues quickly.1112tools: 13 - orders.lookup14 - orders.refund15 - tickets.createFrom code to production in one workflow
agents/invoice-processor.yaml
version: "1.0"name: invoice-processormodel: gemini/gemini-2.5-pro system_prompt: | You are an expert accountant. Extract all fields from invoices. tools: - documents.parse - documents.extract_entitiestools/documents.py
async def parse( file_url: str, format: str = "auto") -> dict: """Parse a document.""" return {"text": text, "pages": n}agents/invoice-processor.yaml
version: "1.0"name: invoice-processormodel: gemini/gemini-2.5-prodescription: "Extracts data from invoices" system_prompt: | You are an expert accountant. Extract all fields from invoices and verify mathematical accuracy. temperature: 0.3tools: - documents.parse - documents.extract_entities - database.store_analysistools/documents.py
async def parse( file_url: str, format: str = "auto") -> dict: """Parse a document and extract text. Args: file_url: URL to the document format: File format (pdf, docx, auto) Returns: Parsed document with metadata """ async with httpx.AsyncClient() as client: response = await client.get(file_url) return { "text": extracted_text, "pages": page_count, "format": detected_format }Any model. Any system. One platform.
Use your own API keys with any provider. Connect agents to the infrastructure you already run.
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Frequently Asked Questions
Everything you need to know about deploying AI agents with Connic
Connic is an enterprise-grade platform for deploying and managing AI agents. You define agents in YAML, write custom tools in Python, and push to Git. Connic handles all infrastructure: deployment, scaling, monitoring, and integrations. No DevOps expertise required.
No. Connic abstracts away all infrastructure complexity. Any developer who can write YAML and Python can deploy production AI agents. There's no Docker, Kubernetes, or CI/CD configuration to manage.
Connectors bridge your agents to external systems. Inbound connectors trigger agents from webhooks, scheduled jobs, or message queues. Outbound connectors deliver results to APIs or services. Sync connectors enable real-time request-response patterns. All configured in the dashboard, no code required.
Connic supports all major LLM providers, including OpenAI, Anthropic (Claude), Google (Gemini), and many more. You can switch models by changing one line in your agent configuration.
Pricing is based on agent run time (minutes) and number of runs. Start free with generous limits. Paid plans include more capacity and features. Only pay for what you use beyond your included allowance. No hidden fees.
Yes! The Knowledge Base feature lets you upload documents, paste text, or programmatically store information. Your agents can then query this knowledge using semantic search. No vector database setup required.
Connic provides built-in observability: run history, execution traces, token usage tracking, and real-time status updates. Click on any run to see exactly what happened, step by step. No additional monitoring tools needed.
Yes! The free tier includes generous usage limits to get you started. No credit card required. Upgrade anytime as your needs grow.