Most AI agents break in production.
Here is how to build ones that do not.

It depends on the task structure. LangGraph is well-suited for workflows with branching logic and state that needs to persist across steps. CrewAI works well when you want distinct agent roles collaborating on a task. LangChain is a reasonable default for linear retrieval-augmented generation and tool-calling pipelines. AutoGen fits research-style multi-agent conversations. We assess your specific use case, your team's ability to maintain the code, and your infrastructure before recommending anything.

For simple, narrow tasks - yes, with the right scaffolding and low-code tooling layered on top. For anything that touches multiple systems, requires reliable output formatting, or runs autonomously on production data, you need engineering involvement at least during the build and testing phases. We can build and hand off, build and maintain, or upskill your internal team depending on what makes sense for your situation.

You need tracing and evaluation in place. That means logging every LLM call with its inputs, outputs, and latency, running structured evals against known test cases, and monitoring for output drift over time as your data or prompts change. Without this infrastructure, you are flying blind. We treat observability as a first-class deliverable, not an afterthought added after something breaks.

RAG is the pattern of pulling relevant documents or records from a vector store or search index and including them in the prompt context before the LLM generates a response. It matters any time you want an agent to answer questions about your own data - contracts, product documentation, CRM notes, support tickets - rather than relying on general training knowledge. Getting the chunking, embedding model, and retrieval logic right is where most RAG implementations underperform.

Token costs vary significantly by model choice, context window size, and call frequency. An agent that passes large documents to GPT-4o on every run will cost materially more than one using a smaller model with targeted retrieval. We design for cost efficiency by right-sizing models to tasks, caching where appropriate, and avoiding unnecessary LLM calls. We also help you forecast operational costs before you commit to a production architecture.

A focused, single-purpose agent with clean data inputs and a defined output format can reach a reliable production state in a few weeks. Multi-agent systems with complex routing, multiple tool integrations, and stateful memory take longer - typically several weeks to a few months depending on data readiness and integration complexity. The biggest variable is almost always data quality, not the framework itself.

Both, depending on what fits. Open-source frameworks like LangChain and LangGraph give you full control and avoid vendor lock-in, which matters for mid-market firms that want to own their stack. Proprietary platforms like certain no-code agent builders can accelerate simple use cases but introduce dependency and cost risk at scale. We are vendor-agnostic and will tell you honestly when a proprietary tool is the right call and when it is not.