The Six Types of AI Agents in 2025

By Dr Luke Soon

As we enter 2025, we have crossed a threshold from the era of generative AI into the era of agentic AI. No longer are AI systems simply assistants generating text or images on demand—they are becoming autonomous, goal-directed agents that can plan, reason, act, and collaborate across digital ecosystems under human-defined guardrails.

Recent research confirms this shift. A 2025 global survey of 3,466 senior executives revealed that 88% of early adopters of agentic AI are already realising positive ROI on at least one use case . These organisations report measurable gains in productivity, customer experience, security, and revenue growth, proving that agentic AI is no longer theoretical but operational at scale.

In this piece, I break down the six major categories of AI agents that business and technology leaders must understand in 2025—each with distinct architectures, technical enablers, and enterprise implications.

1. UI Interaction Agents

These agents operate directly on user interfaces, interpreting visuals, text, and interactive elements. By combining Visual Language Models (VLMs) with General Purpose LLMs, they can simulate human interactions with browsers, text editors, or ERP front ends.

Technical enablers: Multimodal models, computer vision pipelines, OCR/NLP hybrids. Enterprise impact: Automating tasks like form-filling, UI testing, and RPA augmentation, reducing reliance on brittle scripted bots. Example: Agents that execute end-to-end workflows in legacy applications without needing backend API access.

2. Workflow Automation Agents

Workflow automation agents are orchestration platforms that stitch together APIs, triggers, and applications into coherent processes. Unlike traditional workflow engines, they use AI-driven reasoning to dynamically assign subtasks to specialised agents.

Technical enablers: Agentic orchestration frameworks (LangGraph, AutoGen, CrewAI), function-calling APIs, event-driven microservices. Enterprise impact: Transforming business process management by embedding intelligence into orchestration layers. Example: A claims processing pipeline where documents are read by one agent, validated by another, and escalated when anomalies are detected.

3. Knowledge Retrieval Agents

At their core, these agents are retrievers plus generators. They query large databases, data lakes, or vector stores, using retrieval-augmented generation (RAG) techniques to answer complex user queries with grounded evidence.

Technical enablers: Vector embeddings, hybrid retrieval (semantic + keyword), knowledge graphs, fine-tuned LLM generators. Enterprise impact: Knowledge management at scale—support desks, legal research, scientific discovery. Example: Agents that synthesise technical documentation, compliance updates, and case law into structured, explainable answers.

4. Coding & Development Agents

These agents assist developers by reasoning about code, debugging, generating modules, and even simulating test environments. The most advanced versions combine LLM reasoning with execution sandboxes for safe testing.

Technical enablers: Code-specialised LLMs (e.g., Codex descendants, StarCoder2), AST (abstract syntax tree) manipulation, reinforcement learning from developer feedback. Enterprise impact: Shrinking SDLC (software development lifecycle) timelines, reducing dependency on offshore coding pools, and democratising software creation. Example: An AI agent that refactors legacy COBOL into Java or Rust, while simultaneously generating documentation.

5. Tool-Specific Agents

These are focused, API-bound agents optimised for narrow tasks such as sending emails, querying databases, or executing trades. Unlike workflow agents, they are not orchestration layers but highly specialised workers.

Technical enablers: API connectors, plugin ecosystems, guardrailed function-calling. Enterprise impact: Augmenting knowledge workers by taking over routine tasks, freeing them for strategic activities. Example: A treasury desk agent that automatically reconciles payment files with ERP records.

6. Voice Interaction Agents

Voice agents leverage speech-to-text (STT) and text-to-speech (TTS) systems to mediate natural language conversations. Increasingly, they are multimodal, capable of detecting sentiment, intent, and even emotional tonality.

Technical enablers: Transformer-based STT/TTS, diarisation models, telephony integration (SIP, WebRTC). Enterprise impact: Revolutionising call centres, customer support, and accessibility. Example: AI receptionists handling Tier-1 customer queries, escalating only when complex negotiation or empathy is required.

The ROI Imperative

The taxonomy above is not academic—it is already reshaping industries. According to recent benchmarks:

70% of executives report productivity gains from AI agents . 63% report improved customer experience, particularly in retail and financial services . 55% report marketing uplift through campaign automation . 49% report stronger security posture, with faster incident detection and resolution .

Early adopters dedicate 50% of their AI budgets to agents, embedding them across operations . The message is clear: agentic AI is not just an R&D experiment, but a productivity flywheel with measurable financial impact.

Beyond Taxonomy: Towards Multi-Agent Systems

The six types of agents outlined are building blocks. The next frontier lies in multi-agent collaboration, where heterogeneous agents interact—UI agents feeding workflow agents, knowledge retrievers empowering coders, and voice agents front-ending tool-specific agents.

This multi-agent orchestration mirrors organisational design itself: specialised roles coordinating toward shared objectives. Research from Stanford HAI, MIT Sloan, and WEF suggests that agent societies may become the dominant computational paradigm of the 2030s, accelerating the convergence of human–machine ecosystems.

Conclusion

2025 marks the inflection point of agentic AI. Every organisation, regardless of industry, has workflows where AI agents can deliver value. The challenge for leaders is not whether to adopt, but how to design responsible agent ecosystems with trust, safety, and ROI at their core.

Those who succeed will not only automate processes but reimagine work itself—moving us closer to a future where humans and intelligent agents co-create value in ways that were previously unimaginable.