Product updates, engineering insights, and everything new in the world of AI agent infrastructure.
AI agents that delete data, process refunds, or call external APIs need a safety net. Connic Approvals pause agent execution at critical moments, wait for human review, and resume automatically — giving you control without killing autonomy.
A/B testing, agent guardrails, API spec tools, dashboard templates with percentile metrics, migration CLI, and more.
You deployed AI agents. How do you know they are actually good? Learn how to set up automated evaluation with LLM judges that score every run against custom criteria.
You changed the prompt. It feels better. But is it actually better? Learn how to run controlled experiments on your AI agents and let real traffic decide.
Your LangChain prototype works. Now you need it to handle real traffic. Learn how to migrate existing agent code to a production-grade platform without rewriting from scratch.
Shipping AI agents without a security strategy is a liability. A practical checklist covering prompt injection, PII handling, output validation, and the guardrails you need before go-live.
Learn when to use Connic's document database for structured CRUD vs. the knowledge base for semantic search. Configuration tips and best practices.
Connic Guardrails intercept agent inputs and outputs in real time to block prompt injection, redact PII, and enforce topic restrictions.
Managed database, templates library, evaluation judges, Telegram connector, web page reading, persistent sessions, conditional tools, and concurrency rules.
Connic Bridge creates a secure outbound tunnel so your AI agents can reach private Kafka, databases, and internal services without opening inbound ports.
Custom observability dashboards with drag-and-drop widgets, model pricing for cost tracking, refreshed connector and runs UI, and llms.txt support.
Deploying AI agents without visibility is flying blind. Learn how to build custom dashboards, track LLM costs per model, and catch failures before users do.
Your demo works great until you have 1,000 concurrent users. A practical guide to the production requirements most teams discover too late.
Stripe connector with webhook signature verification, Email connector with IMAP polling and attachment support, plus dashboard UI improvements.
No more manual uploads or YAML guessing. The Composer SDK brings scaffolding, validation, hot-reload testing, and CLI deployments.
"We'll just deploy it on Kubernetes" - famous last words. The true cost of self-hosting AI agents vs. a managed platform.
Your customers expect AI features, but you don't have ML engineers. Learn how teams ship AI agents using skills they already have.
MCP connector exposing agents as tools, Postgres LISTEN/NOTIFY, S3 file uploads, SQS message queues, connector logs, and unified connector UI.
Your AI agent answers beautifully, just not with your company's information. Learn how RAG transforms generic chatbots into domain experts.
10 deployment regions across 5 continents, import predefined tools (trigger_agent, query_knowledge, web_search) in custom Python code.
Full audit logging with before/after diffs, data residency region selection, distributed rate limiting for connectors, and billing cost breakdown.
