Connic is an AI agent deployment platform that enables developers to build, deploy, and manage production AI agents without infrastructure expertise. It provides a managed runtime environment where teams define agents declaratively using YAML configuration files, write custom tools as standard Python functions, and deploy through a Git-based workflow that handles scaling, monitoring, and integrations automatically. Connic is designed for software engineering teams and SaaS companies that need to move AI agents from prototype to production. Typical users include backend developers building AI-powered features into existing products, DevOps teams looking to avoid the complexity of self-hosting agent infrastructure, and product teams deploying customer-facing AI workflows such as support automation, document processing, or data enrichment. The platform addresses a common bottleneck in AI agent development: the gap between a working prototype and a production-grade system. Running agents at scale requires isolated execution environments, concurrency control, retry logic, observability, and integrations with external systems. Connic handles these concerns so developers can focus on agent logic rather than infrastructure. Key capabilities include: - Declarative agent configuration: Agents are defined in YAML with support for LLM agents, sequential pipelines, and tool-calling agents. Custom tools are written as typed Python functions. The Composer SDK validates configurations locally before deployment. - Git-based deployment: Pushing to a connected Git repository triggers automatic builds and versioned deployments. Every deployment supports instant rollback. A CLI alternative is available for teams not using Git integrations. - Enterprise connectors: 12 built-in connectors bridge agents to external systems, including webhooks, cron schedules, Apache Kafka, Amazon SQS, Amazon S3, PostgreSQL (via LISTEN/NOTIFY), WebSockets, MCP (Model Context Protocol), email (IMAP), Stripe, and Telegram. Connectors can be inbound (triggering agents), outbound (delivering results), or synchronous (request-response). - Built-in observability: Every agent run is recorded with full execution traces, token usage tracking, latency metrics, and cost breakdowns. Custom dashboards allow teams to monitor agent performance across deployments and environments. - Knowledge base and managed database: Agents can query uploaded documents via semantic search for retrieval-augmented generation (RAG), or use a managed relational database for persistent state and structured data storage. Connic supports all major LLM providers, including OpenAI, Anthropic, Google, and others, using the team's own API keys. Additional platform features include guardrails for real-time input/output filtering, human-in-the-loop approval workflows for sensitive agent actions, A/B testing with traffic splitting, and automated evaluation using LLM-based judges. The platform offers a free tier with no credit card required, along with paid plans starting at $390 per month. Pricing is based on agent run time (minutes) and number of runs, with pay-per-use billing beyond included limits. Connic is available in 10 deployment regions and provides SOC 2 security practices, a data processing agreement, and EU AI Act compliance documentation.