Product updates, engineering insights, and everything new in the world of AI agent infrastructure.
Which AI agent platforms keep data in the EU? The 2026 shortlist grouped by residency model, and what residency must cover: traces, storage, model calls.
An MCP connector links an AI app to external tools and data over the Model Context Protocol. Learn how it works and when it beats a custom API integration.
The Connic Marketplace is the new home for agent templates, connectors, and knowledge ingest sources. One catalog, one publisher model, one CLI install.
Customizable permission groups, shareable dashboards with access controls, channels and drains for routing notifications and logs, sharper prompt-injection and data-exfiltration guardrails, AI-assisted filtering, and deployments that drain in-flight runs.
Webhook, Kafka, Postgres LISTEN/NOTIFY, and SQS each trigger an AI agent differently. Compare their delivery guarantees, ordering, and durability to pick one.
How DACH teams build, trigger, and run production AI agents in 2026 — adoption, model mix, connectors, cost, reliability, and compliance, from Connic customer data.
Production agents have to receive events from and send results to the systems around them. Pre-built connectors turn that plumbing into a platform feature instead of custom code you write and maintain.
A practical, step-by-step path to shipping your first production AI agent with a small team: scope one job, define it in config, connect it to your existing systems, and let a runtime handle the rest.
The real cost of assembling your own AI agent stack isn't the tools — it's the integration and maintenance tax between them, and when buying a platform wins.
Run production AI agents in the EU without US hyperscalers: what EU-hosted must really mean, where the US CLOUD Act exposes you, and a sovereignty checklist.
An agent testing framework, deploy gates with pull-request testing, deeper tracing for triggered and child runs, usage and budget dashboards, custom domains for connectors, and per-agent reasoning effort with cascading defaults.
Ranked shortlist of AI agent platforms evaluated on EU data residency, self-hosting, MCP tool support, BYOK, EU AI Act readiness, and SLA terms. Updated July 2026.
A line-item TCO model at 50,000 AI agent runs/month, comparing self-hosted Kubernetes, Connic Pro (subscription-as-credit with uniform per-unit rates), and Inngest. EUR throughout, with cited sources.
A YAML-driven testing framework built for non-deterministic AI agents. Statistical pass thresholds, expression-based assertions, tool-call checks, tool mocking, multimodal fixtures, and a deployment gate that blocks broken builds.
Human-in-the-loop approvals, Bridge for custom tools and private services, tool hooks, discoverable tools, AI dashboard builder, custom OpenAI-compatible providers, and live logs from your own code.
Agent-native runtimes, workflow engines, framework stacks, or plain frameworks? Compare the 4 platform types on lock-in, pricing, and connectors.
The EU AI Act is the world's first comprehensive AI regulation, and it applies to your AI agents today. Here's what it requires, what the penalties look like, and how Connic makes compliance the default rather than an afterthought.
How to pause an AI agent before refunds, deletes, or external calls, route the decision to a human, and resume automatically, with a full audit trail.
A/B testing, agent guardrails, API spec tools, dashboard templates with percentile metrics, migration CLI, and more.
Automated agent scoring uses an LLM judge to grade every agent run against criteria you define. Set up AI agent evaluation that tracks quality trends and alerts on regressions.
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. 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 find out about 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 adds 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.
