Glossary

What is an AI agent?

An AI agent is an autonomous software entity that perceives its environment, processes information, and acts to achieve specific goals. Modern agentic AI systems coordinate specialized agents through memory and feedback loops, enabling them to reason through complex problems and adapt their responses. These systems excel at enterprise workflows by leveraging machine learning (ML), natural language processing (NLP), and large language models (LLMs) to transform raw data into valuable business insights.

How do AI agents work?

AI agents operate by perceiving their environment, reasoning about their goals, and executing actions to achieve those goals. They combine large foundation models with specialized capabilities such as memory, which allows them to retain and reuse contextual information, and reflection, which lets them assess and adjust their actions based on feedback. Using these capabilities, agents can dynamically plan, execute, and re-plan as conditions change—rather than following rigid, rule-based sequences. In enterprise environments, agents often collaborate within multi-agent systems, where a dynamic planning agent coordinates specialized agents and tools to complete complex workflows. This architecture enables agents to act autonomously, accurately, and adaptively across real-world business processes.

What are the benefits of using AI agents?

AI agents address reasoning and decision-making challenges that would be difficult to handle with conventional software. Their significance comes from several technical capabilities:

1. Advanced reasoning for decision support.AI agents can analyze complex scenarios, generate multi-step plans, and make context-aware decisions that support a wide range of business and operational use cases. Unlike traditional rule-based systems, they reason dynamically, evaluating alternatives, prioritizing actions, and synthesizing data to recommend or execute optimal outcomes.
2. High accuracy through memory and learning.By retaining short- and long-term memory, AI agents can recall relevant information, past actions, and successful outcomes to improve future performance. This contextual grounding enhances precision, consistency, and auditability in environments where accuracy is critical, such as forecasting, diagnostics, and compliance.
3. Dynamic adaptability and orchestration.AI agents can continuously adjust their plans and actions as new data or conditions emerge. In enterprise settings, this adaptability allows them to coordinate multiple agents and tools, manage dependencies across systems, and maintain reliable performance even as processes scale or evolve.

Together, these capabilities enable AI agents to deliver trustworthy, high-value outcomes in data-intensive, fast-changing environments — transforming how organizations plan, decide, and operate.

What are examples of AI agents?

AI agents are used across a wide range of enterprise functions, where they automate reasoning, decision-making, and coordination tasks traditionally performed by humans.

Sales, marketing, and customer service

In customer-facing roles, AI agents can analyze behavioral data to recommend products, personalize marketing outreach, and respond to service inquiries with contextual accuracy. They can route cases, summarize interactions, and provide next-step recommendations to improve customer engagement and retention. Enterprises see accelerated case resolution times, and more time freed for employees to focus on higher-value, strategic activities.

Back office operations

In functions such as finance, HR, legal, and procurement, AI agents automate repetitive and data-intensive workflows.
They can reconcile transactions, process invoices, review contracts, generate reports, and assist in talent management — improving efficiency while maintaining compliance and auditability. This reduces administrative burden, allowing teams to allocate more attention to decision-making and strategic business initiatives.

Operations and supply chain management

AI agents in operations and manufacturing environments monitor production systems, forecast demand, and optimize logistics. They can identify equipment anomalies, recommend schedule adjustments, and coordinate across suppliers to minimize delays and maintain business continuity. These capabilities enhance resilience and efficiency, empowering organizations to respond proactively to changing market or production conditions.

Research and development

In engineering and scientific domains, AI agents accelerate innovation by processing large datasets, running simulations, and assisting with design exploration or experimental analysis. They can suggest hypotheses, automate testing cycles, and surface insights that guide discovery and development. This helps enterprises bring new products and technologies to market faster while enabling researchers to focus on creativity and problem-solving.

Across these domains, AI agents enable organizations to augment decision-making, streamline complex workflows, and achieve higher levels of accuracy and adaptability in daily operations.

How does C3 AI use AI agents?

The C3 Agentic AI Platform incorporates AI agents through a patented multi-hop orchestration framework that coordinates specialized agents to perform complex, multi-step enterprise workflows. This framework underpins C3 AI Agentic Process Automation (APA) — a core capability of the platform that enables organizations to automate dynamic business processes using reasoning, planning, and adaptive decision-making. With APA, enterprises can declaratively model end-to-end workflows that integrate human-style reasoning with enterprise-grade governance, security, and traceability.

Our model-driven architecture allows these agents to seamlessly connect with existing enterprise data and systems through a unified semantic data model. This eliminates the need for custom integrations while ensuring consistent visibility, auditability, and control across all agentic operations.

C3 AI delivers a comprehensive portfolio of prebuilt applications powered by these agents, including C3 AI Reliability, C3 AI Inventory Optimization, and C3 AI Agentic Process Automation. For example, agents within our supply chain applications autonomously perform data retrieval, optimization modeling, and visualization tasks to help organizations continuously adapt to changing market conditions.

Major enterprises across manufacturing, energy, and financial services use C3 AI’s agentic solutions to automate complex workflows, enhance decision quality, and accelerate operational performance across the business.