AI agents represent a shift from assistive AI to autonomous, outcome-focused systems. While the benefits of AI agents include scalability, continuity, and smarter integration, success depends on strong governance. This guide looks at AI agents through a leadership lens, focusing on where they create sustainable advantage and where they introduce new risk. Infosys BPM enables organisations to orchestrate agentic workflows that balance autonomy with control and deliver measurable business value.
what is an AI agent? (the core definition)
An AI agent is an autonomous software system that leverages large language models to observe its environment, reason through information, plan actions, and execute tasks to achieve defined objectives with minimal human intervention.
Unlike conversational interfaces that respond only to user prompts, AI agents operate with intent. They work towards outcomes, not interactions. With defined goals, constraints, and access permissions, agents can independently progress work across systems and processes.
At a functional level, what an AI agent is comes down to three characteristics:
- Autonomy, where the agent initiates actions based on goals or events.
- Continuity, where context persists across steps and decisions.
- Outcome orientation, where completed work is the measure of success.
This shift allows AI to assume responsibility for process execution rather than task support.
AI agents vs AI assistants: what is the difference?
AI assistants and AI agents share similar foundations but serve different enterprise roles. The distinction affects how organisations design workflows, assign accountability, and manage risk.
| Feature | AI assistant | AI agent |
| Activation | Responds to direct user prompts | Acts on defined goals, rules, or events |
| Decision-making | Suggests or drafts | Decides and acts within set boundaries |
| Workflow scope | Isolated tasks | End-to-end, multi-step workflows |
| System access | Limited, often read-only | Controlled write access across systems |
| Oversight model | Constant human involvement | Human-on-the-loop governance |
While AI assistants enhance individual productivity, AI agents extend operational capacity by running processes, coordinating systems, and escalating only when needed.
how do AI agents work? (the architecture)
AI agents operate through an architectural loop that combines reasoning, memory, planning, and action. This structure allows agents to operate reliably within enterprise environments.
contextual memory and knowledge grounding
Agents maintain short-term memory to track task progress and decisions. Long-term memory connects them to enterprise knowledge using retrieval mechanisms, ensuring responses stay grounded in approved data rather than assumptions.
goal-driven planning and reasoning
Planning engines break high-level objectives into executable steps. For example, a compliance review becomes data gathering, validation checks, exception handling, and reporting. The agent evaluates outcomes at each step before proceeding.
controlled action through tools
Tools define how an agent interacts with the real world. These include APIs, databases, ERP platforms, CRM systems, and communication tools. Guardrails ensure actions remain authorised and auditable.
Together, these components explain how AI agents work beyond simple prompt-response models.
types of AI agents in the enterprise
Enterprises deploy different types of AI agents depending on process complexity, risk, and scale requirements.
specialised task agents
These agents focus on a single, well-defined function such as contract review, anomaly detection, or data reconciliation. Their narrow scope improves accuracy and simplifies governance.
adaptive generalist agents
Generalist agents handle broader requests across domains. They support functions like employee support or customer engagement, adapting responses while still operating within policy and system constraints.
collaborative multi-agent systems
Multi-agent systems distribute work across coordinated agents. One agent gathers data, another analyses it, and a third executes actions. This model supports complex workflows without creating a single point of failure.
Choosing the right type of AI agent is critical for balancing flexibility, control, and performance within enterprise workflows.
what are the business benefits of AI agents?
The benefits of AI agents extend beyond efficiency gains to drive structural improvements across enterprise workflows.
Enhanced productivity - Agents enable continuous operations, allowing processes to run without time-zone or capacity constraints.
Scalability - AI agents improve scalability by handling large volumes of concurrent requests without proportional increases in cost.
Legacy integration - AI agents act as an intelligent coordination layer, connecting modern AI capabilities with legacy platforms through APIs and workflows, reducing the need for costly system replacements.
Over time, these benefits of AI agents translate into faster cycle times, improved consistency, and more resilient operations across finance, procurement, HR, IT, and more.
enterprise challenges and governance risks
Autonomy introduces new considerations that enterprises must manage carefully.
- Hallucinations: Reasoning errors can lead to incorrect actions if agents rely on incomplete or outdated information.
- Infinite loops: Without clear stopping rules, agents may also repeat tasks unnecessarily, creating inefficiencies.
- Security:AI agents require carefully defined entitlements to prevent unauthorised data access or actions. Auditability, version control, and approval checkpoints are essential for maintaining trust.
Addressing these risks early ensures organisations capture value while maintaining compliance and accountability.
how Infosys BPM orchestrates AI agents
Infosys BPM approaches AI agents as part of a broader operating model, not isolated automation. The focus lies on orchestration across people, processes, and platforms.
Orchestration frameworks manage how agents interact with enterprise systems and when human intervention becomes necessary. High-risk actions trigger approval checkpoints, while low-risk tasks proceed autonomously, maintaining both speed and control.
Infosys BPM applies this model across use cases such as invoice reconciliation, automated KYC, and IT service desk resolution. Each deployment aligns agents with business rules, compliance standards, and measurable outcomes.
By embedding AI agents into governed workflows, Infosys BPM helps enterprises move from experimentation to scalable, responsible execution.
FAQs on AI agents
are AI agents the same as RPA?
No. RPA automates predefined rules and breaks when conditions change. AI agents reason through ambiguity, adapt to new information, and decide next actions dynamically, making them suitable for complex, variable processes.
can AI agents work offline?
Most AI agents rely on cloud-based language models and enterprise systems, requiring connectivity. While edge AI is evolving, offline operation remains limited to narrow, low-complexity scenarios today.
