Problem/Solution Report: Cascade

The Problem: The Breakdown of Traditional Monitoring As enterprises transition from simple chatbots to complex, autonomous AI agents, traditional monitoring and observability tools are reaching their limits. Traditional systems rely on static evaluations and manual human oversight, which cannot scale with the non-deterministic nature of AI agents. When an agent executes a multi-step workflow, it can fail in unpredictable ways—hallucinating, misusing tools, or following flawed reasoning. Reactive fixes often arrive too late, and manual reviews are too slow to prevent 'cascading failures' where one error in an autonomous loop triggers a series of operational breakdowns. The core problem is that as AI grows more autonomous, it becomes harder for humans to ensure it remains safe and reliable at scale.

The Solution: Self-Improving Evaluation Infrastructure Cascade provides the infrastructure necessary to run AI agents reliably in production by treating agent execution as data. Instead of relying on static rules, Cascade builds systems where safety and reliability models continuously improve themselves. The platform observes real production runs and trains 'evaluator models' that learn what correct behavior looks like within a specific company's unique workflows. This allows the system to detect emerging threats and adapt evaluations in real-time, rather than waiting for a human to identify a new failure mode.

Value Proposition and Approach Cascade's approach involves analyzing every step of an agent's process, including its reasoning steps, tool usage, and final outcomes. By automatically detecting failure modes and reliability issues, Cascade converts these evaluations into structured feedback. This feedback is then used to improve the underlying prompts, rubrics, and models. The result is a virtuous cycle where the more an agent operates, the more the safety infrastructure learns, ultimately preventing failures before they impact operations. This enables companies to deploy autonomous intelligence with the confidence that the system is self-correcting and resilient to the complexities of real-world production environments.

Market and Competitors Report: Cascade

Market Landscape and Trends Cascade operates in the rapidly evolving AI Observability and MLOps market. The primary trend driving this market is the shift from 'Model-centric' AI (where the focus is on the LLM itself) to 'Agent-centric' AI (where the focus is on the autonomous actions the model takes). As enterprises move beyond pilot programs into production-grade autonomous agents, the demand for 'AI-driven observability' has surged. Market data indicates that over 54% of enterprises have now adopted some form of AI-powered monitoring. The target audience includes enterprise engineering teams, AI safety officers, and DevOps professionals who are responsible for the reliability of autonomous workflows in sectors like finance, healthcare, and customer operations.

Competitive Landscape Cascade faces competition from several directions within the AI infrastructure stack:

• Model Observability Specialists: Companies like Arize AI, WhyLabs, and Fiddler AI are established leaders in monitoring model performance, drift, and bias. While they are expanding into agent monitoring, Cascade's focus on 'learned evaluator models' specifically for agent workflows is a key differentiator.
• Agent Tooling and Evaluation: LangSmith (by LangChain) and Guardrails.ai provide tools for testing and setting boundaries for AI agents. These are often used during the development phase, whereas Cascade positions itself as production-grade infrastructure for continuous, self-improving reliability.
• AIOps and Observability Incumbents: Large players like Datadog, Dynatrace, and Splunk are integrating AI monitoring into their massive existing platforms. Their advantage is their existing footprint in enterprise IT, but they may lack the specialized, deep-learning-based evaluation models that Cascade offers for autonomous agents.
• Orchestration Platforms: Tools like Temporal, Prefect, and Airflow manage the execution of complex workflows. While they ensure a process runs, they do not typically evaluate the 'correctness' or 'safety' of the AI's reasoning within that process.

Competitive Advantages and Disadvantages Cascade's primary advantage is its 'safety-first' architecture that treats agent execution as data to train specific evaluator models. This allows for a more nuanced understanding of 'correct behavior' than simple keyword-based or heuristic-based guardrails. By converting evaluations into structured feedback to improve the underlying models, Cascade offers a closed-loop system for reliability. A potential disadvantage is the company's early stage (founded in 2025, YC W26 batch), which may make it challenging to compete for large enterprise contracts against established incumbents like Datadog or Arize AI who have broader platform capabilities and longer track records.

Founders and Background Report: Cascade

Cascade was co-founded by Adam AlSayyad and Haluk Cem Demirhan, both of whom bring deep technical expertise in artificial intelligence, machine learning safety, and large-scale systems. The duo met or collaborated through the University of California, Berkeley, specifically within the prestigious Berkeley Artificial Intelligence Research (BAIR) Lab. Their combined backgrounds bridge the gap between cutting-edge academic research in AI agents and the practical challenges of maintaining production-grade infrastructure.

Adam AlSayyad (Co-Founder & CEO) Adam AlSayyad serves as the Chief Executive Officer of Cascade. Before founding the company, he was a researcher at the BAIR Lab at UC Berkeley. His research focused on graph reasoning models and agentic safety, working under some of the world's leading experts in machine learning and AI safety. This academic foundation in how autonomous agents reason and how to ensure their safety is a core pillar of Cascade's mission. Adam holds a degree in Computer Science from UC Berkeley.

Haluk Cem Demirhan (Co-Founder & CTO) Haluk Cem Demirhan is the Co-Founder and Chief Technology Officer. Like Adam, Haluk was a researcher at the BAIR Lab, where his work specifically addressed long-horizon memory optimization and the development of failure mode taxonomies for AI agents. Beyond academia, Haluk brings significant industry experience in building production monitoring infrastructure and scaling agent systems at major technology firms, including Netflix and Amazon. He studied Computer Science and Mathematics at UC Berkeley.

Together, the founders leverage their experience in identifying how AI agents fail and their history of building robust monitoring systems to create a platform that makes autonomous intelligence safe and reliable. Their leadership is defined by a unique blend of theoretical safety research and practical engineering at the scale of the world's largest streaming and e-commerce platforms.