Businesses are built on workflows. These workflows standardize processes and protect key business interests, enabling systems and employees to complete tasks effectively.
AI is changing the nature of business workflows. AI agents can interpret their environment, gather information and take targeted actions in response. When applied to business workflows, these agents can create agentic AI workflows: autonomous systems that build, operate and adapt workflows based on business needs and conditions.
How do agentic workflows work?
Agentic AI workflows fall under the broader umbrella of agentic process automation, a subset of business process automation (BPA). In general, automating manual workflows can offer significant benefits; traditional workflows often use resources inefficiently, fail to adapt to new conditions and go years without meaningful updates.
Agentic workflows rely on machine learning (ML) techniques, particularly large language models (LLMs) and natural language processing (NLP). These technologies enable agents to gather and interpret information, respond to user requests, understand business context, and engage with both human workers and other IT systems. Agentic workflows can also adapt in real time to changing conditions, optimizing performance continuously.
Agentic AI workflows typically unfold in four broad phases:
- Task analysis. The AI agent analyzes existing workflows and identifies the tasks required for execution. It usually interoperates with existing IT or BPA systems to visualize and map these tasks.
- Task creation. Based on its analysis, the agent builds automation elements to carry out workflow tasks. It can then delegate these tasks to sub-agents -- for example, a pricing agent in an e-commerce system or a customer service agent on a helpdesk platform.
- Task execution. All involved agents work together to execute the workflow. If issues arise, errors are flagged for review and remediation. If the workflow is completed successfully, agents can log execution details in a management dashboard for human review.
- Assessment and adaptation. The AI agent gathers workflow results, performance metrics and UX feedback to evaluate effectiveness. It can then identify opportunities to improve efficiency, reduce costs and optimize the workflow.
Agentic AI components
Agentic AI systems are composed of software elements designed to manage workflows and drive business outcomes. A typical AI agent includes seven core components:
- Goals. The agent is driven by defined goals, which guide behaviors, provide benchmarks for success and can evolve alongside business needs.
- Perception. The agent gathers business information and interprets it in context. This sensory capability typically involves accessing historical and real-time data. For example, a logistics agent might analyze real-time traffic and weather data to optimize shipping routes.
- Processing. The heart of an AI agent is its processing engine -- usually a sophisticated LLM -- that enables the agent to reason through problems; make predictions; and link inputs to goals, plans and actions.
- Planning. The agent develops a course of action to achieve its objectives, either by organizing existing tasks or creating new ones.
- Synchronization. The agent delegates tasks to subagents operating within a shared memory space and ensures that they coordinate their actions to carry out the overall plan.
- Action. The agent executes the plan, managing task dependencies and troubleshooting as needed to complete the workflow.
- Learning. The agent continuously learns by analyzing results and generating reports for human review. This continuous feedback loop refines planning and improves the agent's future performance.
Benefits and limitations of agentic AI workflows
Agentic AI workflows build on many strengths of traditional automation and orchestration techniques. Their benefits include the following:
- Efficiency. Agentic workflows can automate more complex and nuanced tasks than standard BPA systems, reducing bottlenecks, accelerating approvals and handling a wider range of variations.
- Accuracy. By collecting and analyzing more data -- often in real time -- agentic workflows can support more informed decisions, detect anomalies and surface insights that conventional tools might miss.
- Agility. Agentic workflows can adapt dynamically to shifting business conditions, realigning to new goals with little, if any, human interaction.
- Scalability. Agentic systems can distribute tasks across subagents to handle enormous volumes of work without sacrificing performance.
- Cost savings. Agentic workflows can reduce operational costs compared to their manual counterparts by executing large-scale tasks more quickly and efficiently.
However, despite these benefits, agentic AI workflows also present several challenges:
- Infrastructure. Agentic systems require significant computing infrastructure -- including servers, storage and networking -- to support real-time data processing and decision-making.
- Data quality. Agentic workflows are only as good as their data. Incomplete, biased or otherwise low-quality data can degrade outcomes. Agentic workflows should also adhere to ethical data use standards, especially for workflows in high-stakes contexts such as hiring and healthcare.
- Integration. Agentic workflows often build on existing workflows, requiring integration capabilities that can raise issues around data accessibility, interoperability and scalability.
- Security. Because almost all corporate data sources are subject to some form of oversight, strong safeguards are essential to meet governance and compliance requirements.
- Explainability. As agentic workflows become more autonomous, their decisions can be harder to explain. This opacity and risk of unintended consequences complicate troubleshooting, compliance and user trust.
Agentic workflow use cases
Agentic AI workflows have broad applicability across industries:
- Help and support. Agents can personalize responses to customer questions using contextual understanding, customer history and business logic. They can also predict customer needs and trigger support actions.
- Manufacturing. Agents can streamline production, inventory management and warehousing by responding to sales orders, stock levels and user requests. They can also monitor manufacturing equipment in real time and predict failures to enable preventive maintenance.
- Finance. Agents can identify potential fraudulent transactions in real time and perform detailed risk assessments for loans and insurance by analyzing applicant data, market trends and banking records.
- Healthcare. Agents assist with real-time patient diagnoses based on symptoms, test results, medical imaging and medical histories. They can also generate treatment plans that take into account insurance coverage, medication availability and provider schedules.
- Logistics. Agents can recommend inventory management based on demand forecasts and stock levels. They can also optimize delivery routes using data on traffic, weather, carrier location and vehicle condition.
- Government. In smart cities, agents can monitor traffic, manage power distribution and detect signs of emergencies.
Best practices for agentic workflows
Agentic AI adds insight and autonomy to decision-making -- but effective implementation requires careful planning. Best practices for agentic AI workflows include the following:
- Define clear goals. Goals serve as benchmarks for evaluating agents' performance, so setting well-defined objectives is critical.
- Use specialized agents. Like human workers, AI agents have unique focuses, strengths and weaknesses. This means that assigning the right agent to the right task is essential.
- Foster collaboration. Although specialization matters, agents must also work together, synchronizing their actions and output to ensure transparency and reliability.
- Keep humans involved. Collect meaningful metrics and KPIs using alerts, reports and real-time dashboards, which human managers can review to ensure that AI actions continue to meet business needs.
- Ensure data and AI governance. Agentic workflows rely on large volumes of data. Document and validate data sources, and create AI governance policies to enforce transparency, oversight and security across the enterprise.
Stephen J. Bigelow, senior technology editor at Informa TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.