Types of AI Agents
Not all AI agents are the same. The right architecture depends on your use case, the level of autonomy required, and how much structure exists in your workflows.
Conversational Agents
Intelligent dialogue, at scale
Conversational agents handle natural language interactions with customers, employees, or systems. They go far beyond scripted chatbots, using LLMs to understand intent, maintain context across turns, and respond with nuance.
Core Capabilities
- ✓Multi-turn dialogue with persistent memory
- ✓Intent classification and entity extraction
- ✓Seamless handoff to human agents when needed
- ✓Integration with CRM, ticketing, and knowledge bases
- ✓Support for voice, chat, email, and messaging channels
Architecture Note
Built on a retrieval-augmented generation (RAG) foundation, these agents access a curated knowledge base on every turn to ensure accuracy. Tool-use lets them look up live data, create records, and trigger actions without leaving the conversation.
Best For
- Customer support
- HR self-service
- Sales qualification
- Onboarding
Autonomous Agents
Plan, act, and complete tasks without supervision
Autonomous agents decompose complex goals into steps, execute those steps using tools, evaluate their own output, and iterate until the task is done. They require minimal human input once launched.
Core Capabilities
- ✓Goal decomposition and multi-step planning
- ✓Tool use: web search, code execution, API calls, file I/O
- ✓Self-evaluation and error recovery
- ✓Long-running task management (hours to days)
- ✓Human-in-the-loop checkpoints for critical decisions
Architecture Note
These agents use a ReAct or plan-and-execute loop, maintaining a scratchpad of observations and actions. A supervisor layer monitors progress and can pause, redirect, or escalate based on confidence thresholds.
Best For
- Research and analysis
- Data pipeline automation
- Report generation
- Competitive monitoring
Multi-Agent Systems
Specialist agents, coordinated like a team
Multi-agent systems orchestrate multiple specialised agents working in parallel or sequence. Each agent handles one domain while a coordinator routes tasks, aggregates results, and manages dependencies.
Core Capabilities
- ✓Parallel task execution across specialised agents
- ✓Dynamic routing based on task type and complexity
- ✓Shared memory and cross-agent context passing
- ✓Fault tolerance with fallback agents
- ✓Real-time monitoring of agent-to-agent communication
Architecture Note
An orchestrator LLM manages a pool of specialist agents, each with its own tools, memory, and system prompt. The orchestrator assigns tasks, tracks completion, resolves conflicts, and synthesises final outputs.
Best For
- Full automation of business workflows
- Complex research requiring multiple domains
- Software development pipelines
- Multi-step financial analysis
Which Agent Type Is Right for You?
The choice depends on three dimensions: task complexity, required autonomy level, and integration depth. Many mature AI deployments combine all three layers. A conversational agent handles front-line interactions, autonomous agents run background operations, and multi-agent systems coordinate complex cross-functional workflows.
We help you map your use cases to the right architecture from day one, preventing costly rebuilds later.