By Nikhil Krishnan, Senior Vice President and Chief Technology Officer, Data Science
Artificial intelligence has advanced rapidly in recent years, and AI agents have become one of the most important developments in the field. These autonomous programs can reason, make decisions, and complete complex tasks across enterprise systems.
C3 AI builds AI agents for production environments where reliability and clarity are essential. They operate within defined roles and connect directly to enterprise systems to complete tasks that would otherwise require manual effort or custom development. This blog looks at how these agents are designed, where they’re used, and why they’ve become a critical part of Enterprise AI.
Beyond Chatbots: What Are AI Agents?
Autonomous AI agents are built to carry out defined tasks inside enterprise systems such as ERP platforms (e.g., SAP, Oracle NetSuite), CRM systems (e.g., Salesforce, Microsoft Dynamics), supply chain management tools, financial systems, and IT service management platforms. They operate independently, use logic to reach outcomes, and don’t require ongoing supervision. While chatbots focus on conversation, agents interact directly with data and workflows in these systems — for example, reconciling invoices in SAP, updating customer records in Salesforce, generating demand forecasts in supply chain tools, or automatically resolving IT service tickets — to deliver specific, measurable results.
What makes these agents effective is their ability to operate within clear boundaries while adjusting to new inputs. Retrieval agents connect to data sources and extract relevant information. Others are built for execution — running models, generating outputs, or using external tools. Agents with memory can track prior steps and apply context as they work. In more advanced cases, built-in feedback mechanisms allow them to revise their outputs during the process.
Types of AI Agents Built for the Enterprise
AI agents vary by function. Each one is built to solve a specific problem inside the broader enterprise workflow.
Retrievers pull targeted information from structured and unstructured sources. For example, a retriever might scan SAP, Oracle, or internal data lakes to gather inventory data, maintenance records, or transaction histories. These results feed into downstream agents that rely on accurate context to make decisions.
Code agents generate and execute logic inside the system. They can create scripts to transform data, run optimization models, or calculate performance metrics based on enterprise rules. In production settings, they’re used for forecasting, anomaly detection, and process simulations, all without manual scripting.
SQL agents turn natural language into structured queries, using fuzzy matching and schema awareness to reduce errors. In finance or sales operations, these agents let users ask questions like “What were the top revenue drivers by region last quarter?” and return results as usable reports or visualizations.
Critics evaluate the outputs of other agents. They check for logic errors, data mismatches, or compliance violations. If an agent produces a questionable result, like an allocation that violates a supply constraint, the critic flags the issue or triggers a revision. This feedback loop improves accuracy over time.
Visualization agents convert raw outputs into formats that decision makers can act on. They generate dashboards, summaries, or annotated reports aligned with user roles. A planner sees projected shipping delays by supplier; a field operator sees maintenance risk tied to specific equipment.
Some agents call external tools or APIs, such as weather data, regulatory feeds, or supply chain trackers. Others run inside the platform for full control and security. Hybrid agents combine both methods, depending on what the task requires. Choosing the right mix of agents depends on the complexity of the workflow and the level of control the system needs.
Tradeoffs and Coordination: Choosing the Right Agent Strategy
There is no universal agent design that works for every use case. Some enterprise tasks are simple and repeatable, while others require coordination across systems, data types, and logic paths. The architecture must be designed to match the problem.
Single-agent systems are easy to configure and deploy. They work well for tasks like data retrieval, basic reporting, or fixed-format transformations. These agents are fast and reliable, but they’re constrained by their scope. They can’t adapt to dynamic conditions or reroute tasks based on context.
Multi-agent systems offer more flexibility. They split work across specialized agents, each responsible for a specific part of the process. One agent might retrieve time series data, another runs a model, and a third formats the output. This structure supports complex workflows, but requires an agentic orchestration layer to keep everything aligned.
The tradeoff comes down to scale and adaptability. Single agents are cheaper to deploy but limited in use. Multi-agent systems cost more upfront and need stronger oversight, but they enable broader coverage and greater resilience. Some tasks need strict logic and speed, while others demand real-time adjustment and context awareness. Choosing the right strategy depends on which of those outcomes matters most in your enterprise.
Inside Real-World Workflows: AI Agents in Action
Enterprise agents are already embedded in daily operations, handling work that spans systems, formats, and decision layers. The examples below show how different agent types function inside production environments to deliver useful, traceable results. These workflows exemplify the capabilities of multi-hop orchestration agents, which employ iterative reasoning and adaptive coordination to manage complex tasks across enterprise systems.
Structured Query for Financial Analysts
A financial analyst opens a dashboard and types, “What were the top factors driving customer attrition last quarter?” That prompt triggers a sequence. A planning agent identifies the relevant sources. An SQL agent translates the question into a structured query with schema-aware mapping and error handling. A code agent runs a lightweight model to surface correlations. The result is returned as a ranked list, visualized by a reporting agent and summarized in natural language for fast review.
Predictive Maintenance Across Industrial Equipment
Sensor data from pumps, turbines, or generators flows into a monitoring system. A retrieval agent identifies outliers in vibration or temperature readings. A second agent pulls maintenance logs and usage history for matching assets. A code agent compares current conditions to known failure patterns. When a risk threshold is met, the system generates an alert with supporting context, visualized directly on a digital twin of the asset. Field teams see not just the risk but why it triggered, what happened before, and what to check next.
Call Center Workflow with Memory and Feedback Agents
A healthcare call center rep asks for a member’s coverage history. A retrieval agent gathers recent case records, benefits data, and past inquiries from multiple systems. A memory-enabled agent identifies that the member called twice before with the same question. A critic agent flags an inconsistency between past responses and current plan details. The system highlights the discrepancy, suggests a corrected explanation, and delivers a complete summary that the rep can read directly to the caller.
These workflows show how generative AI agents operate with purpose inside real systems. Each one contributes to a larger process without adding complexity or breaking the flow of work.
Why Multi-Agent Systems Are Essential for Enterprise AI
AI agents make it possible to manage complexity in a structured, controlled way. Each agent can focus on a tightly scoped task, like querying SAP, generating SQL, or validating a model output, while simultaneously, the orchestration layer ensures the work moves forward in the right sequence. This structure replaces brittle, all-in-one models with modular systems that are easier to govern, scale, and adapt.
The specialization inherent in each agent also reduces risk. Instead of relying on a single model to interpret intent, retrieve data, and return results, multi-agent solutions, like C3 AI Agentic Process Automation, distribute that responsibility across agents and tools with well-defined roles. These purpose-built roles improve reliability and help prevent errors that arise when general models attempt to handle domain-specific logic without constraints.
Because of the feedback loops built into their foundations, AI agents improve over time. With long- term memory, these systems track what’s already been done and applies relevant context to new tasks. Reflexion agents, a type of agent designed to self-evaluate and improve their outputs during or after completing a task, take this improvement a step further. By reviewing their own outputs, they catch mistakes early, and adjust the process without human intervention. These capabilities support continuous refinement without manual retraining or pipeline disruption.
At scale, multi-agent systems reduce the cost of change. Instead of refactoring an entire model pipeline, teams can update a single agent or introduce a new function without touching the rest of the workflow. This structure supports continuous improvement while keeping operations stable.
AI Agents at C3 AI, Built on the C3 Agentic AI Platform
At C3 AI, our AI agents are purpose-built for production-grade enterprise environments, where reliability, traceability, and clarity are essential. These agents are designed to operate within well- defined roles, connecting directly to enterprise systems to complete tasks that would otherwise require manual effort, custom development, or fragile integrations.
The foundation of these agents is driven by the robust, flexible C3 Agentic AI Platform. The platform utilizes a model-driven architecture to accelerate delivery and reduce the complexities of developing enterprise-scale AI applications. Within the C3 Agentic AI Platform, the agentic orchestration layer enables the success of C3 AI’s multi-agent systems. This allows businesses to set up multi-agent workflows with a combination of rule-based logic and the dynamic reasoning capabilities of AI agents.
AI agents don’t succeed on logic alone: they depend on structure. Within the C3 Agentic AI Platform, that structure starts with a unified semantic data model, the C3 AI Type System. This model gives every agent a consistent view of enterprise data, allowing the agents to reason across data sources without needing to translate those data points into a consistent format, or build net-new connections between specific systems or integrations. This removes ambiguity and accelerates time-to-output.
The C3 Agentic AI Platform provides more than a shared data foundation — it offers the tooling to build, govern, and deploy agents at speed. The C3 AI Type System gives every agent a unified view of enterprise data; on top of that foundation, developers can design, configure, and manage agents through C3 AI Studio, a key development environment within the C3 Agentic AI Platform. C3 AI Studio enforces platform standards automatically, ensuring that every agent inherits the same access policies, logging conventions, and task definitions. New agents slot cleanly into existing workflows instead of creating exceptions or one-off implementations.
The C3 AI Code Assistant extends this foundation by helping teams build agents faster and more consistently. Powered by C3 Generative AI, it generates code, logic flows, and task definitions that align with platform patterns — including the C3 AI Type System, access controls, and orchestration rules. Instead of simply suggesting snippets, it produces complete, runnable agent workflows that are ready for review, testing, and deployment. This reduces development effort, shortens approval cycles, and ensures new agents adhere to established system standards from day one.
By default, every agentic action is traceable. Whether pulling source data, executing a model, or passing results to another service, the system records what happened, when, and why. These records are then used to improve performance, tune agent behavior, and support safe iteration. The C3 Agentic AI Platform gives agents a fixed foundation to operate within, so outputs stay consistent no matter how enterprise workflows evolve.
Get Started with C3 AI Agents
C3 AI agent systems power manufacturing asset monitoring, financial analysis, and energy logistics planning with enterprise performance demands. These agents excel where speed and reliability matter most. The agentic orchestration layer in the C3 Agentic AI Platform allows agents to work together through multi-hop reasoning and feedback mechanisms. All actions remain traceable through our unified semantic data model, the C3 AI Type System. Built on over 15 years of research, development, and investment in Enterprise AI, and $3 billion in platform development, we hold the exclusive patent for agent orchestration frameworks. This foundation provides the infrastructure enterprises need to evolve from experimental AI initiatives to orchestrated, production-grade deployments.
Take the next step today: schedule your personalized demo to see our agents in action, or explore our customer success stories to discover how organizations like yours trust C3 AI to power their most critical operations across manufacturing, energy, finance, and more.
About the Author
Dr. Nikhil Krishnan is Senior Vice President and Chief Technology Officer, Data Science at C3 AI and is responsible for Data Science and Generative AI. Prior to C3 AI, Dr. Krishnan was an Associate Principal at McKinsey & Company, where he was a leader in McKinsey’s Advanced Industrials and Energy Practices. Dr. Krishnan was formerly an Assistant Professor at Columbia University. He also worked as a research scientist at Applied Materials, Inc. Dr. Krishnan earned a bachelor’s degree from the Indian Institute of Technology, Madras, and MS and PhD degrees from the University of California at Berkeley in Mechanical Engineering.
