What AI Agent Are You Building in 2026? Share Your Stack, Challenges & Lessons

im messing around with a local agent for summarizing long research papers. definately sticking to pure python scripts right now because frameworks kinda make it hard to debug when things go sideways lol. keeping the memory layer simple has been the biggest challenge so far.

I've spent the last year entirely focused on the infrastructure side of this, so here is a look under the hood of what we are building.

What problem does it solve? We noticed that reasoning frameworks are incredible right now, but they all hit the exact same wall in production: the agents lack deep context. If an agent needs to answer a query that spans a Slack thread, a Jira ticket, and a SharePoint contract, standard RAG just dumps unrelated text chunks into the prompt and the agent hallucinates. We are building an open-source context layer called PipesHub to fix this enterprise data bottleneck.

Which models? We are model-agnostic on the backend, but we generally see the best reasoning results using Claude 3.5 Sonnet and GPT-4o for the actual orchestration loops.

Framework / SDK? Our architecture sits underneath the orchestration layer. So whether a developer is using LangGraph, CrewAI, or the newer OpenAI Agents SDK, PipesHub acts as the unified memory and retrieval engine feeding the right context into those frameworks.

MCP, RAG, Memory? We use all of them, but we handle RAG a bit differently. We use an event-streaming setup (Kafka) to ingest enterprise data continuously. Instead of just dumping it into a vector DB, we route the data into a Vector DB AND a Knowledge Graph. We use MCP as the standard protocol to pipe that unified memory into the agent. This lets the agent dynamically choose how to retrieve data—hitting the graph for hard relationships/lineage, and the vector DB for broad context.

Biggest challenge? "Context rot" and permission fidelity. It is incredibly hard to ensure an agent only retrieves and acts on data that the specific end-user actually has permission to see across dozens of different enterprise silos.

Best practice to share? Stop blindly dumping chunked documents into your prompt. Shift to agent-driven retrieval where the agent first queries a graph to understand the relationship between entities before it tries to read the raw text. Your token costs will drop, and your hallucinations will practically disappear.

Building an A-share financial analysis agent. Indexed three years of financial reports with cross-company comparison by industry and region, plus structured parsing of financial notes.Biggest headache so far is the notes parsing ,every company formats them differently, same accounting item called different things across filings. Getting the model to extract consistently has been a pain.Curious if people think this direction is worth pursuing.