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Orchestrating Self-Evolving Agents with CrewAI and NVIDIA NemoClaw | CrewAI

CrewAI Blog ·

A source describing CrewAI and NVIDIA NemoClaw as tools for building autonomous, long-running AI agents; citing claims about scale of use, infrastructure-level security enforcement, and persistent cognitive memory for agents; all statements attributed to the same author. Lisez 4 points de vue avec leurs éléments à l’appui et les liens vers les sources.

João (Joe) Moura
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En un coup d’œil

  • Shift to autonomous, self-evolving agents

    In early 2026, AI is moving beyond simple prompt-based interactions toward autonomous, long-running agents that can break down goals, execute code, and operate independently — a shift likened to the rise of the modern web stack.

    Lire le moment probant · Paragraphe 2

  • CrewAI enterprise scale claims

    The article reports that CrewAI has powered roughly 2 billion agentic executions over the past year and is currently used by more than 60% of Fortune 500; it describes this as demonstrating readiness for enterprise workloads.

    Lire le moment probant · Paragraphe 11

  • Infrastructure-level policy enforcement

    NVIDIA OpenShell Runtime enforces every action at the infrastructure level—not within the agent’s own code—so even if an agent’s internal logic changes or behaves unexpectedly, the runtime blocks actions violating defined security policies.

    Lire le moment probant · Paragraphe 29

  • Persistent cognitive memory for agents

    CrewAI provides persistent cognitive memory for both Crews and Flows, enabling agents to retain information across sessions, accumulate knowledge over time, and strategically forget — shifting from stateless interactions to learning-capable, long-running workflows.

    Lire le moment probant · Paragraphe 14

Passages clés4

Passages attribués et accompagnés du contexte nécessaire à leur vérification. Ouvrez le texte original pour vérifier la source.

Paragraphe 11 (s’ouvre dans un nouvel onglet)
enterprise adoption metrics

CrewAI enterprise scale claims

Extrait original

This architecture has already proven itself at scale. CrewAI has powered roughly 2 billion agentic executions over the past year and is currently used by more than 60% of Fortune 500, demonstrating readiness for enterprise workloads.
João (Joe) Moura·
Paragraphe 29 (s’ouvre dans un nouvel onglet)
runtime security design

Infrastructure-level policy enforcement

Extrait original

A key innovation is that the NVIDIA OpenShell Runtime enforces every action at the infrastructure level, not within the agent's own code. This means that even if an agent's internal logic changes or behaves unexpectedly, the runtime will still block any action that violates defined security policies .A key innovation in NemoClaw is that every action is enforced at the infrastructure level , not within the agent’s own code. This design ensures that even if an agent’s internal logic changes or behaves unexpectedly, the runtime will still block any action that violates defined security policies.
João (Joe) Moura·
Paragraphe 2 (s’ouvre dans un nouvel onglet)
AI agent evolution

Shift to autonomous, self-evolving agents

Extrait original

The technology landscape in early 2026 is undergoing a shift similar to the rise of the modern web stack. AI is moving beyond simple prompt-based interactions toward autonomous, continuously evolving agents. These long-running agents, which many experienced recently on a more personal level through “claws”, can break down goals, execute code, and operate independently for extended periods.
João (Joe) Moura·
Paragraphe 14 (s’ouvre dans un nouvel onglet)
agent memory architecture

Persistent cognitive memory for agents

Extrait original

In addition, both Crews and Flow and its agents get a persistent cognitive memory layer, allowing them to retain information across sessions and gradually accumulate knowledge, consolidating and even strategically forgetting over time. This shift, from stateless interactions to agents with memory, marks an important step in optimizing long-running agent workflows that can learn from executions itself and human feedback.
João (Joe) Moura·
Contexte

Persistence is also essential when agents need to pause for human input or recover from interruptions. CrewAI’s @persist() decorator automatically saves workflow state so execution can resume later.

Source et méthodologie

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