AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt.
Their capabilities are made possible in large part by the multimodal capacity of generative AI and AI foundation models. AI agents can process multimodal information like text, voice, video, audio, code, and more simultaneously; can converse, reason, learn, and make decisions. They can learn over time and facilitate transactions and business processes. Agents can work with other agents to coordinate and perform more complex workflows.
As explained above, while the key features of an AI agent are reasoning and acting (as described in ReAct Framework) more features have evolved over time.
AI assistants are AI agents designed as applications or products to collaborate directly with users and perform tasks by understanding and responding to natural human language and inputs. They can reason and take action on the users' behalf with their supervision.
AI assistants are often embedded in the product being used. A key characteristic is the interaction between the assistant and user through the different steps of the task. The assistant responds to requests or prompts from the user, and can recommend actions but decision-making is done by the user.
AI agent | AI assistant | Bot | |
Purpose | Autonomously and proactively perform tasks | Assisting users with tasks | Automating simple tasks or conversations |
Capabilities | Can perform complex, multi-step actions; learns and adapts; can make decisions independently | Responds to requests or prompts; provides information and completes simple tasks; can recommend actions but the user makes decisions | Follows pre-defined rules; limited learning; basic interactions |
Interaction | Proactive; goal-oriented | Reactive; responds to user requests | Reactive; responds to triggers or commands |
AI agent
AI assistant
Bot
Purpose
Autonomously and proactively perform tasks
Assisting users with tasks
Automating simple tasks or conversations
Capabilities
Can perform complex, multi-step actions; learns and adapts; can make decisions independently
Responds to requests or prompts; provides information and completes simple tasks; can recommend actions but the user makes decisions
Follows pre-defined rules; limited learning; basic interactions
Interaction
Proactive; goal-oriented
Reactive; responds to user requests
Reactive; responds to triggers or commands
Every agent defines its role, personality, and communication style, including specific instructions and descriptions of available tools.
AI agents can be categorized in various ways based on their capabilities, roles, and environments. Here are some key categories of agents:
There are different definitions of agent types and agent categories.
One way to categorize agents is by how they interact with users. Some agents engage in direct conversation, while others operate in the background, performing tasks without direct user input:
AI agents can enhance the capabilities of language models by providing autonomy, task automation, and the ability to interact with the real world through tools and embodiment.
Increased output: Agents divide tasks like specialized workers, getting more done overall.
Simultaneous execution: Agents can work on different things at the same time without getting in each other's way.
Automation: Agents take care of repetitive tasks, freeing up humans for more creative work.
Collaboration: Agents work together, debate ideas, and learn from each other, leading to better decisions.
Adaptability: Agents can adjust their plans and strategies as situations change.
Robust reasoning: Through discussion and feedback, agents can refine their reasoning and avoid errors.
Complex problem-solving: Agents can tackle challenging real-world problems by combining their strengths.
Natural language communication: Agents can understand and use human language to interact with people and each other.
Tool use: Agents can interact with the external world by using tools and accessing information.
Learning and self-improvement: Agents learn from their experiences and get better over time.
Realistic simulations: Agents can model human-like social behaviors, such as forming relationships and sharing information.
Emergent behavior: Complex social interactions can arise organically from the interactions of individual agents.
While AI agents offer many benefits, there are also some challenges associated with their use:
Tasks requiring deep empathy / emotional intelligence or requiring complex human interaction and social dynamics – AI agents can struggle with nuanced human emotions. Tasks like therapy, social work, or conflict resolution require a level of emotional understanding and empathy that AI currently lacks. They may falter in complex social situations that require understanding unspoken cues.
Situations with high ethical stakes – AI agents can make decisions based on data, but they lack the moral compass and judgment needed for ethically complex situations. This includes areas like law enforcement, healthcare (diagnosis and treatment), and judicial decision-making.
Domains with unpredictable physical environments – AI agents can struggle in highly dynamic and unpredictable physical environments where real-time adaptation and complex motor skills are essential. This includes tasks like surgery, certain types of construction work, and disaster response.
Resource-intensive applications – Developing and deploying sophisticated AI agents can be computationally expensive and require significant resources, potentially making them unsuitable for smaller projects or organizations with limited budgets.
Organizations have been deploying agents to address a variety use cases, which we group into six key broader categories:
Customer agents
Customer agents deliver personalized customer experiences by understanding customer needs, answering questions, resolving customer issues, or recommending the right products and services. They work seamlessly across multiple channels including the web, mobile, or point of sale, and can be integrated into product experiences with voice or video.
Employee agents
Employee agents boost productivity by streamlining processes, managing repetitive tasks, answering employee questions, as well as editing and translating critical content and communications.
Creative agents
Creative agents supercharge the design and creative process by generating content, images, and ideas, assisting with design, writing, personalization, and campaigns.
Data agents
Data agents are built for complex data analysis. They have the potential to find and act on meaningful insights from data, all while ensuring the factual integrity of their results.
Code agents
Code agents accelerate software development with AI-enabled code generation and coding assistance, and to ramp up on new languages and code bases. Many organizations are seeing significant gains in productivity, leading to faster deployment and cleaner, clearer code.
Security agents
Security agents strengthen security posture by mitigating attacks or increasing the speed of investigations. They can oversee security across various surfaces and stages of the security life cycle: prevention, detection, and response.
Google Cloud provides a portfolio of products and solutions in the AI agent space. These include integrated AI assistants, pre-built AI agents, AI applications, and a platform of agent and developer tools to build custom AI agents.
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