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Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.
Long-horizon reasoning exposes a core weakness in AI agents: context windows fill up fast, and retrieval pipelines return noise instead of signal.To solve this, researchers at the National University [...]
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs
For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]
A 0.12% parameter add-on gives AI agents the working memory RAG can't
AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and [...]
Tencent's Team Memory shares AI agent memory across a team — with no governance yet for when it's wrong
A VB Pulse survey this June found that 57% of enterprises had traced a confidently wrong agent answer back to missing or inconsistent context — the latest sign of how central context has become to w [...]
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]
'Observational memory' cuts AI agent costs 10x and outscores RAG on long-context benchmarks
RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, thos [...]
57% of enterprises have watched AI agents be confidently wrong. The fix is an agentic context layer, but who has one?
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pull [...]
ACE prevents context collapse with ‘evolving playbooks’ for self-improving AI agents
A new framework from Stanford University and SambaNova addresses a critical challenge in building robust AI agents: context engineering. Called Agentic Context Engineering (ACE), the framework automat [...]