Topic
observability
Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents
A new research paper formulates memory retention in long-horizon language agents as a constrained stochastic optimization problem, proposing OSL-MR (Observability-Safe Learning for Memory Retention). The method combines an evidence learner with a Mixed-Score heuristic, achieving superior performance under tight budgets on benchmarks LoCoMo and LongMemEval. The work establishes a principled foundation for memory management in AI agents.
Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability
A research paper proposes a trace-based observability framework for multi-agent LLM systems that diagnoses wasted computation before final evaluation. On 165 GAIA traces, warned failed runs spent 58.1% of tokens after the first warning. A pilot using warnings reduced post-warning token fraction from 0.638 to 0.304, supporting a layered design with cheap online signals and deeper semantic checks.
Technology Coralogix raises $200M on bet that someone needs to watch the AI agents
Coralogix has raised $200 million in Series F funding, valuing the observability startup at $1.6 billion. The round, led by Advent and CPPIB, comes as enterprises increasingly deploy AI agents that require new monitoring tools. The company's revenue grew over 60% in the past year, and more than half of enterprise customers now use its AI agent Olly or other AI interfaces.