Seven Principles of Ambient Agents

In the first post in this series, I described a set of “ambient agents” working together to improve an online shopping experience. Ambient agents are found in the arena: both reactive to changes in their environment and proactive about solving problems without being instructed.

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Your agent should be capable of proactive problem-solving. The irritating package delivery that needs rescheduling? You merely express the intent: “This is a pain point for me, deal with it.” … Your agent takes over, interfacing directly with the vendor’s AI agent. It analyzes your calendar for true availability (understanding your preference for uninterrupted deep work blocks or the variability of picking your kids up from dance class), negotiates a new slot, and updates your schedule, all without further intervention. It simply informs you of the resolution.

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Our agents need to be autonomous operators, so that they are not limited in their effectiveness by the availability – and capability – of their human operators. If we are to realize the full potential of agents, we need to give them the right span of control to be as effective as possible.

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Ambient agents aren’t ‘coin-operated’, stuck waiting for external input from humans. They know what their goal is, they know what their action space is, and they independently make decisions and take action in pursuit of those goals.

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Note that autonomous operation does not mean that there aren’t other agents or humans in the loop for authorization purposes. Like a military officer, the agent has a clear span of control, but decisions that fall outside of that span have to be sent up the “chain of command” for approval.

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For any data architects or engineers reading this – think of this as the ambient agent consuming the gold-layer signal, not the bronze-layer data (per the Medallion Architecture). Stream processing is essential to turn the firehost of raw events into clear signals that can be incorporated into the agent’s context window (pull), or delivered in real-time to the agent via Kafka events or Akka messages (push).

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Today, most agentic applications rely on the world-ontology that is baked into the underlying LLM through its own training data. But given how critical semantic reasoning is, it’s not surprising that more AI engineers are working to encode this understanding formally, and build agents that can develop that semantic representation over time. In pursuit of this, engineers are embracing semantic layers, knowledge graphs and ontologies; VCs are also joining the dots.

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The end game for semantic reasoning includes the entire loop of perception, semantic interpretation, action prediction, decision-making, and learning from outcomes – it’s about integrating all environmental information into a coherent semantic framework to guide decision-making and continuously improve. A good place to learn more is the 2024 paper ‘Unifying Large Language Models and Knowledge Graphs: A Roadmap’.

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LLMs are a fundamentally stateless technology – but an ambient agent needs to have a memory like an elephant, not like a goldfish. Being set a long-term goal, an ambient agent has to be able to remember its observations, its actions over time, and its own impact on the environment and other agents; in short, it needs to be able to track its progress against its goal.

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Long-term persistence is the central design concept of a set of “agentic memory” startups, including Cognee, Mem0 and Zep. There is also some interesting work coming out of ServiceNow Research, with their idea of “tape agents”, a skeuomorphic concept of a central and shared cassette tape that stores all of the history of these agents.

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Ambient agents are not a new type of agent. Instead, they represent the coming wave of architectural patterns for building sophisticated, goal-oriented, agents. These principles put the high agency – the autonomy – into AI agents.

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The seven principles are as follows:

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The autonomous principle is so clear and compelling that it is starting to appear in vendor marketing for AI agents, for example from Bloomreach, emphasis mine:

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Ambient agents continuously monitor the world as things happen, responding to their real-time observations rather than waiting for explicit instructions.

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For this principle, I am deliberately using the term ‘perception’ rather than the more common language of ‘event-driven’. Why? Ambient agents don’t consume the raw firehose of every granular environmental event, any more than my prefrontal cortex directly consumes my eyeballs’ rod and cone data.

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These agents will be assembled inside each enterprise, inside each SaaS vendor, and across organizational boundaries; this is what Google’s Agent2Agent Protocol exists to facilitate.

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Asynchronous communication via event streams

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seven principles: continuous perception for ambient agents.

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