What is an AI agent?
An AI agent (also known as an autonomous agent) is a system that uses artificial intelligence, often powered by large language models (LLMs), to perform tasks, make decisions, and learn from its environment. AI agents are characterized by their ability to take action toward a predetermined goal over an extended period of time—with limited or no human intervention.
That is, given a goal, an AI agent can create a plan that breaks down the high-level goal into multiple smaller tasks, execute them using the tools available to it, reason over its findings at every step, and make adjustments to the plan to eventually achieve the goal.
How do AI agents work?
AI agents incorporate technologies such as large language models (LLMs), real-time data analysis, and natural language processing (NLP) as they interact with inputs and data. These technologies enable AI agents to reason, make decisions, and complete tasks. Here is an overview of the key steps in AI agent functionality:
Inputs and data ingestion: AI agents gather structured data, such as Excel files, SQL Databases, metadata, and actions, in addition to unstructured data, such as chat conversations, call transcripts, documentation, and code repositories. Data ingestion provides AI agents with a contextual understanding of a task.
Decision-making and reasoning: Most agents have a reasoning step where they make decisions or plan out what to do next based on data ingested in the previous step. Reasoning can involve a variety of tools or capabilities but is task- and agent-specific. For some tasks, an agent might be planning multiple steps to get to a goal, while in other cases, an agent may decide to take a specific action but have no other plan.
Action: After the reasoning step, the AI agent might have an action to take or might be waiting for the next time it gets input or is triggered. These actions can involve any number of task-specific capabilities such as reading a web page, triggering scripts, sending alerts, adjusting configurations, and so on.
Learning and adaptation: Many AI agents incorporate learning capabilities. Through reinforcement learning and machine learning techniques, these agents can refine their decision-making processes by learning from previous actions and outcomes. This adaptation enables them to improve performance and effectively respond to changing conditions.
How is using an AI agent different from directly using a large language model?
AI agents have “agency,” enabling them to perceive, plan, and act in a multi-step, goal-driven process. This design removes basic one-step classification or response-type interactions (that is, the need to ask your language model a question). In comparison to AI agents, legacy chatbots (also called chat agents) are designed as simple question-and-answer–type bots with no goal orientation. An AI agent’s primary distinction from interacting with a language model is that the agent can run in a self-directed, proactive way, largely augmented by a lightweight prompting layer, and with persistence or memory enabling the agent to handle multi-turn/step interactions over time.
What are the benefits of using AI agents?
AI agents can automate repetitive tasks, enhance decision-making, and increase efficiency and adaptability while reducing the need for human intervention. For further information, consider the following:
Automation of repetitive tasks: AI agents can perform multiple steps autonomously, such as monitoring resources, summarizing log reports, and reviewing pipelines. This capability reduces manual steps and frees up resources so teams can focus on innovation and other initiatives. AI agents can also handle labor-intensive tasks, allowing humans to focus on more complex and creative aspects of their work. By automating tasks, AI agents reduce the risk of human error, ensuring that processes are executed consistently and accurately every time.
Real-time decision-making: AI agents can access large amounts of data to help make informed decisions in real time, improving system efficiency . Through semantic or tagged data, AI agents are well-suited to respond to contextual queries. AI agents are also capable of using structured data (such as observability telemetry data, security data, and pipelines), in addition to unstructured data (such as chats, transcripts, code, and documentation), which can enhance the intelligence and comprehension of complex data sources.
Adaptability: AI agents learn from their environments and their training. They can adapt to new data or requirements, making them useful in dynamic environments that frequently change.
What are the implementation challenges regarding AI agents?
AI agents offer many features and advantages, but there can be challenges when trying to implement these agents into an existing environment, such as:
Lack of comprehensive data: AI agents rely on large, high-quality datasets to be effective. Incomplete or reduced data availability can severely limit an AI agent’s ability to provide accurate insights, detect patterns, or make decisions. If data silos or historical data are restricted or limited, an AI agent’s effectiveness might be further reduced.
Explainability and transparency: Because AI agents use language models and machine learning to undertake decision-making, security and compliance officers demand explainability and transparency. Certain decisions or recommendations made by AI agents, especially in regulated industries, can lead to questions regarding transparency and trust.
Cost: Deploying AI technologies, particularly language models and machine learning, can be resource-intensive and costly. Organizations must invest in GPUS and other high-performance computing (HPC) infrastructure, data storage, and ongoing model training to ensure that AI agents are effective and responsive. Integrating AI agents with existing systems while maintaining service-level agreement (SLA) performance requirements can require substantial financial resources. Managing AI-based systems, including continuous monitoring, retraining, and updating of language models, can further increase overall implementation costs.
What are the DevSecOps use cases for AI agents?
AI agents are finding great purpose across industries and markets. Implementations range from code development and completion for software developers to developing sales analysis and marketing reports to integrating back-office workflows, and even range as far as conducting research and writing for different fields. AI agents can be deployed for various stakeholders across an IT organization. Some use cases include:
Incident response: AI agents can automatically process trillions of data points in real time, helping on-call engineers investigate, identify the root cause of an issue, and take action to quickly mitigate the issue’s impact. For DevSecOps, AI agents can identify unusual patterns to detect potential security incidents or intrusions, and they can enforce security policies for code and processes to maintain compliance. They can also act as incident commanders, pulling in the right service owners and guiding troubleshooting.
Natural language queries: AI agents can use natural language processing to interpret queries, allowing IT teams to interact with complex systems. With natural language processing, users can ask questions in plain language (for example, “Why is the CPU usage high on server X?” or “What are the top 5 errors in today’s logs?”). The AI agent interprets these queries, accesses relevant data sources like logs or monitoring tools, and provides detailed answers or visualizations.
Automated triage and self-healing: AI agents can receive incident reports or alert messages and take automated corrective actions based on predefined playbooks. These steps can include restarting a service, scaling resources, or patching vulnerabilities. AI agents can also handle less critical processes so IT teams can focus on managing operations without enduring alert message fatigue.
Code generation and debugging: AI agents can generate code based on natural language descriptions, requirements, and design specifications. Using language models, AI agents can understand programming languages, libraries, and frameworks, enabling them to create efficient, working code tailored to a given request. AI agents can help with debugging by automatically analyzing error messages, identifying problematic code, and suggesting potential fixes.
Industry shifts regarding AI agents
Advancements in AI technologies are changing along with AI agents. By harnessing an LLM, an AI agent’s ability to learn gives IT teams tools that can help them manage infrastructure issues, handle ever-evolving requirements for security and compliance, and use natural language queries that allow users to create reports without complex technical knowledge or programming. AI agents are crucial tools for teams managing complex IT infrastructure.
Conclusion
AI agents are powerful tools for IT organizations to automate and reduce repetitive tasks, investigate alerts, improve resiliency, and increase efficiency. Read how Datadog can improve IT operations through autonomous investigations with Bits AI.
