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An AI that builds other AIs in real time
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with zero coding, zero scripting, and
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full autonomy, is no longer an idea.
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It's live. Emergence AI, founded by
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former IBM researchers, has launched a
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system that generates custom agents on
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the spot using just a text prompt. In
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this video, we're breaking down how this
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new tech works, what recursive
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intelligence actually means, and why it
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could shift how automation works across
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enterprise systems. Let's dive in. how
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this AI actually builds other AIS. At
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the core of emergence AI system is the
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orchestrator, a logic engine that
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evaluates incoming tasks, determines
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what resources are available, and builds
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what's missing. It operates by first
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consulting an internal registry of
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existing agents. If the task can't be
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completed with what's already built, it
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creates new agents on the fly. These
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agents are generated using
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state-of-the-art large language models.
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According to Venturebe, Emergence's
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platform supports models including
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OpenAI's
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GPT4 O and
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GPT4.5, Anthropics Claude 3.7 Sonnet and
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Meta's Llama 3.3. Depending on the task
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and configuration, the orchestrator
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selects the most appropriate model to
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generate and refine agent behavior. But
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this isn't just about generating code.
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Once an agent is built, it's capable of
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executing actions, verifying outcomes,
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and improving through repetition. A
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process emergence calls self-play. The
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orchestrator doesn't stop once the
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agents are generated. It continues to
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monitor performance and learns from task
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outcomes. What separates this system
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from other agent frameworks is its
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complete lack of required manual
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intervention. No pipelines need to be
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set up. No agent libraries need to be
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dragged in. You just describe the goal,
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the rest is handled by the system itself
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using recursive loops to continuously
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reassess and adapt. In practice, this
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means the AI is not only building tools,
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it's deciding what tools need to exist
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in the first place. Orchestrator
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architecture and recursive thinking. The
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intelligence behind this system comes
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from its architecture. The orchestrator
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acts like a manager that's constantly
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asking two questions. Can I solve this
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with what I already have? And if not,
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what do I need to build? When it
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encounters a task it can't solve, it
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generates a new goal. The creation of an
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agent that can. This ability to reflect,
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plan, and create sub goals autonomously
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is what emergence defines as recursive
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thinking. Agents aren't created
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randomly. They're task specific,
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assigned with memory, and given the
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ability to plan and verify their own
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output. Once created, they're stored in
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the internal registry so they can be
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reused for future tasks. During the
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demo, the company displayed a timeline
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of agent creation in real time. Each dot
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represented an agent color-coded by
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function from categorization to data
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extraction to reporting. As tasks
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progressed, the orchestrator became more
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efficient, creating fewer agents and
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relying more on existing ones. This
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addresses one of the common problems in
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multi-agent systems, bloat. If a
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platform creates too many agents for
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every new task, it becomes inefficient.
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But emergence AI's orchestrator was
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designed to consolidate and generalize,
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making the system leaner over time.
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Instead of endlessly building, it learns
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what's reusable, optimizing for
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long-term performance, which is crucial
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for enterprise scale deployments. real
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use cases that matter for the
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enterprise. In 2025, much of the AI
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agent conversation has revolved around
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consumerf facing tools and chatbot
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applications. Emergence AI, however, is
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focusing on enterprise workflows,
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specifically those that are data
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inensive, repetitive, and prone to
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manual bottlenecks. The platform is
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currently being tested across several
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enterprise tasks, including automating
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ETL pipeline creation, managing
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cloud-based data migrations,
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transforming and normalizing large data
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sets, and building dashboards from
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unstructured spreadsheets. It's also
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being used for large-scale text
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summarization and
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classification, tasks that typically
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require custom scripts in engineering
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oversight. These workflows often involve
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repetitive back-end logic that slows
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down teams. Emergence AI's orchestrator
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is designed to eliminate that friction
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by generating and managing task specific
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agents automatically without writing
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code or configuring pipelines manually.
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At the AI engineer world's fair in 2024,
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CEO Satya Nita emphasized that while
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large language models can now generate
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code efficiently, they lack the ability
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to execute or verify it. Emergence AI
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system fills that gap by pairing code
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generation with autonomous agent
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execution and embedded oversight. Rather
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than outputting a script that still
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needs developer involvement, the
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platform produces a working solution
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that runs end to end with checkpoints
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for human validation where needed. This
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shift is part of a broader trend. A
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Gartner report from the first quarter of
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2025 estimates that over 70% of
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enterprises will implement some form of
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AI agent framework by year's end, up
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from just 12% in 2023. The demand is
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driven by the need for tools that can
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adapt quickly to changing business
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requirements without constant
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reconfiguration. Emergence AI system
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doesn't just solve the task at hand. It
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improves across tasks, learns from
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outcomes, and becomes more efficient
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over time.
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Removing the need for constant
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engineering input. What makes this
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platform different from anything else,
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Emergence AI system stands apart from
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most existing agent frameworks by
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focusing on creation, not just
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orchestration. While platforms like
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Langchain, Microsoft's Autogen, and Crew
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AI focus on linking pre-built agents to
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execute tasks in sequence, emergence
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AI's orchestrator builds those agents
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dynamically from scratch. The difference
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is summarized well by CEO Sachinetta's
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phrasing. They orchestrate emergence
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creates. The platform operates through a
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completely no code interface. Users
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interact with it through natural
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language and the orchestrator handles
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everything from agent design to
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execution. There's no need to manually
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select agents or define workflows. The
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system makes those decisions
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autonomously. A key differentiator is
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how it handles complexity over time.
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Instead of endlessly generating new
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agents for every task, the orchestrator
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learns to generalize. It identifies
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patterns across tasks and begins to
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reuse existing agents more efficiently.
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This addresses the issue of agent sprawl
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where systems become bloated with too
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many narrowly focused tools. As more
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tasks are completed, the system builds a
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smarter internal registry. This allows
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it to solve future problems using fewer,
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more versatile agents, reducing
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duplication, optimizing resource use,
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and improving long-term performance
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without additional human input. The
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guardrails, despite its autonomous
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capabilities, Emergence AI's platform is
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built with multiple layers of safety and
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human oversight. These mechanisms are
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designed to ensure that enterprises
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maintain full control over their
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workflows and that the system operates
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within clearly defined boundaries. The
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platform includes access control layers
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to restrict agent creation and task
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execution to authorize users. Each agent
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is evaluated using verification rubrics
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that assess performance, accuracy, and
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adherence to task objectives. If an
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agent doesn't meet predefined criteria,
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it can be flagged or removed before it
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impacts production workflows. Another
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key safeguard is human in the loop
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validation. While the orchestrator can
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act independently, it pauses at critical
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checkpoints to allow for human review.
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This ensures that newly created agents
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are operating as expected and aligned
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with business goals before being
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deployed at scale. Nida has emphasized
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that human oversight remains central to
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the platform's philosophy. In his words,
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you need to verify that the multi- aent
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system or the new agents spawned are
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doing the task you want and went in the
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right direction. Automation is the goal,
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but always within a framework of human
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defined intent and accountability.
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Interoperability and what's coming next.
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Emergence AI system is built for
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flexibility with compatibility across
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multiple models and frameworks. It
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supports OpenAI's GPT 4.0 0 and GPT 4.5,
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Anthropics Claude 3.7 Sonnet and Meta
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Llama 3.3 allowing enterprises to choose
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the model best suited to their task
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requirements. This also means
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organizations can bring their own
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foundation models into the ecosystem. It
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also integrates with major agent
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frameworks such as Langchain, Crew AI,
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and Microsoft Autogen. This makes it
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easier for enterprises to embed the
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orchestrator into existing AI
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infrastructure without needing to
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replace what they have already built.
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Looking ahead, the company has announced
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a major platform update scheduled for
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May 2025. This update will include
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containerized deployment, allowing the
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orchestrator to run in any cloud
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environment. It will also expand the
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systems self-play capabilities, enabling
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agents to simulate task variations and
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improve more rapidly without external
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input. Emergence AI's team includes
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researchers and engineers with
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experience at IBM Research, Google
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Brain, the Allen Institute for AI,
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Amazon, and Meta. The company states
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that it's still early in development,
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but the system is already being tested
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in multiple enterprise settings across
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the US, Europe, and Asia. Why? This
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changes everything long-term. The
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emergence of self-building ai systems
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signals a shift in how tasks are handled
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in both enterprise and technical
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environments. While large language
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models continue to improve at generating
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language and code, they still rely on
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human direction. They can suggest
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solutions, but they don't act. Agentic
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systems close that gap. By combining LLM
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output with autonomous agent execution,
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platforms like emergence AI's
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orchestrator offer a path toward real
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autonomy. Instead of coding tools, we
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now prompt systems to build them,
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reducing the time and expertise required
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to translate intent into action. The
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long-term implications are significant.
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As tasks become more dynamic and
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interconnected, static workflows will
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struggle to keep up. Recursive systems
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that build and evolve their own
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solutions could become the default
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infrastructure behind enterprise
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operations. As this space evolves, the
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question becomes less about what AI can
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do and more about what you would trust
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it to handle. If you've made it this
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far, let us know what you think in the
00:11:09
comments section below. For more
00:11:11
interesting topics, make sure you watch
00:11:13
the recommended video that you see on
00:11:14
the screen right now. Thanks for
00:11:16
watching.
