What are AI agents?

Reactive, basing their actions on preset rules and current data or cues only, simple reflex agents work well for simpler cognitive tasks that require immediate response without extensive reasoning. For example, to reset customer passwords when specific keywords are detected in a conversation. Or for smart homes, to adjust the temperature based on thermostat data.

Using an internal model built on data inputs and perceptions, model-based reflex agents make informed decisions that require context outside of immediate task data, making them effective for more complex tasks that involve predicting outcomes. For example, suggesting driving routes using maps and location updates.

Taking rules to another level, goal-based agents apply reasoning to weigh different paths to achieving a specific outcome in order to choose the most efficient approach. For example, these agents can help with scheduling tasks that must conform to certain deadlines or parameters.

As their name implies, learning agents continuously evolve and improve performance based on experiences and collected data. This capacity to adapt is suited to tasks such as recommendation engines that more closely reflect user preferences as they learn from feedback and interactions.

Designed to assess options for optimal resource allocation or maximum overall outcome based on utility values or benefits, utility-based agents make decisions that align with user preferences and goals. For example, scaling computing resources based on application needs to optimize for performance and costs.

Like managers, hierarchical agents deconstruct complex tasks into smaller ones and assign them to subordinate task-specific agents. The higher-level agent collects the results and coordinates agents to ensure the collective achievement of goals.

Consisting of numerous interacting agents with varying levels of autonomy, multi-agent systems coordinate/compete and communicate with each other to achieve shared objectives. MAS are ideal for solving intricate problems and boosting efficiency in diverse applications.

No-code agent creation

Building AI agents should not require data science expertise. Look for solutions that empower business users (and accelerate developers) with templates and an intuitive agent creation experience.

LLM choice and customization

LLM choice is a significant factor in designing effective AI agents. Grounding in enterprise knowledge enables agents to match the way your business operates for better results, faster. Effective solutions will connect with any large language model (LLM) and enable customization based on enterprise data.

AI skills

AI agents can be made with specific skills to execute business tasks. Skills represent sets of generative AI prompts that are linked together. Look for platforms that facilitate rapid agentic automation by packaging sets of generative AI capabilities into reusable AI skills that others can use for faster agent development.

Integration

As the name implies, agentic automation requires the ability to seamlessly integrate generative AI into automation workflows. Embedding generative AI within automations is the key to enabling AI agents to execute cognitive tasks at scale. The security and compliance framework provided by AI-powered automation platforms will determine the safety and compliance of deploying AI agents. Identify providers that offer integration flexibility via open architecture alongside robust platform security and both AI and automation governance features.

Multi-agent orchestration

AI agents should be able to team up across mission-critical enterprise processes and be integrated into your enterprise architecture. To align and coordinate AI agents, ensure your chosen solution offers robust process orchestration tools and multi-agent orchestration features. Look for a comprehensive suite of capabilities that include intuitive workflow design, plug-and-play integrations and APIs, robust task automation and scheduling, and real-time monitoring and analytics.

AI governance

Full visibility into every AI activity and response is necessary to enforce responsible AI policies and protect company data. Enterprise solutions will enable you to set guardrails for consistent use and human validation, review content within prompts and model responses for sensitivity and relevance, and monitor and audit both agent and model performance. Logs and analytics for both prompts and model responses should feed insights into model performance and accuracy.

Data privacy controls

An enterprise agentic automation solution will be able to comply with industry-specific regulations, adhere to GDPR and data privacy principles, including the encryption of sensitive information, and provide essentials-only cloud storage. Essential capabilities to look for include robust encryption methods such as AES-256 for data at rest and SSL/TLS for communication, secure credential storage and management with privileged access management platform integrations, and strict access controls through RBAC and authentication measures like SAML, MFA, and OAuth. Certifications to trust: SOC 1 Type 2, SOC 2 Type 2, ISO 27001, HITRUST, and ISO 22301.

Multi-pronged security

The agentic automation solution you choose should provide robust, multi-tenant, cloud-native environments with data encryption and adherence to privacy compliance. Compliance with major industry data standards and security certifications is essential to ensure data protection. Look for security certifications such as ISO27001 and SOC 1 & 2 Type 2.

Creating custom AI agents is quickly becoming faster and easier—and accessible without data science expertise. Until recently, building AI agents and agentic automations required expert-level AI developers to connect LLM capabilities with custom-built code to create a unified agent. The nuances and sensitivities of designing and testing LLM prompts represented a significant barrier to development. However, today, new no-code tools to build AI agents are quickly transforming the landscape, bringing agent creation to business users with prebuilt model connectors, the ability to easily add human-in-the-loop, and built-in safety guardrails.

The application of AI agents for business is moving towards multi-agent and agent-to-agent systems. Harnessing systems of linked agents represents a major opportunity for organizations to achieve greater efficiency and collaboration by deploying AI technology. These systems are characterized by communication and coordination between multiple AI agents, driving seamless decision-making for accelerated autonomous operations with optimal resource allocation. By breaking down complex objectives into components that leverage the specific capabilities of individual AI agents, multi-agent systems can orchestrate AI-powered automation across enterprise operations. New advancements in agent-to-agent interactions are enabling collective problem-solving among interconnected AI agents, allowing them to navigate complex business ecosystems both within and between organizations.

Agentic AI and supporting AI technologies have already proven their transformative potential across industries. As businesses embrace and accelerate the development and deployment of AI agents, governance frameworks, ethical considerations, and the nature of regulatory oversight are evolving topics with far-reaching relevance for enterprises. Collaborative efforts at the global level seek to address wide-ranging issues such as verifiable honesty, data protection, transparency, access, and misuse, as well as the need for agile governance and strategic oversight of multi-agent systems. As new standards for governance and responsible AI are defined, they will shape the next wave of AI advancements and their business applications. Implementing agentic automation grounded in accepted governance frameworks and ethical guidelines will establish trust and enable long-term success.