Divya van Mahajan

The 7 Types of AI Agents

ai-daily-brief-podcast

The 7 Types of AI Agents

Overview

This episode of the AI Daily Brief (published July 9, 2025) examines the emerging taxonomy of AI agents, drawing on a recent article from The Information titled “The Seven Kinds of AI Agents” alongside complementary frameworks from AWS, DigitalOcean, and KPMG. The central argument is that agents are no longer experimental — they are actively being deployed across enterprises — and that understanding the different types of agents is essential for forming a coherent agent strategy. The episode is hosted by the creator of the AI Daily Brief podcast and video channel (name not stated). The episode also covers industry headlines including Meta’s talent poaching, OpenAI compensation changes, Cursor pricing controversy, and CoreWeave’s acquisition of Core Scientific.

Source video: URL not provided.

Prerequisites

  • Basic familiarity with large language models (LLMs) and generative AI tools (e.g., ChatGPT, Claude)
  • General understanding of enterprise software and business process automation
  • Awareness of the distinction between AI assistants/copilots and autonomous AI systems
  • Familiarity with terms like multi-agent systems, orchestration, and AI infrastructure is helpful but not required

Main Points

Industry Context: Agents Are Already Being Deployed at Scale

  • KPMG Pulse survey data shows the percentage of organizations with agents fully deployed (past pilot stage) tripled from 11% to 33% between Q1 and Q2 of 2025
  • Pilots grew from 37% to 65% between Q4 2024 and Q1 2025
  • 90% of surveyed organizations are now past the experimentation stage and actively in pilots or full deployment
  • This makes agent taxonomy a practical, not merely theoretical, concern

The Practical Dividing Line: Assistants vs. Agents

  • The speaker argues that hyper-technical definitions of “agent” are less useful than the intuitive business understanding
  • The functional dividing line: assistants are AI you use to do things; agents are AI that do things for you
  • This common-sense framing is described as “more functionally useful” than narrow academic definitions
  • Understanding subcategories matters because different agent types serve different strategic purposes

Framework 1 — Functional Categories (How Agents Operate)

Drawn from AWS and DigitalOcean definitions, this framework organizes agents by their internal decision-making mechanisms:

  • Simple Reflex Agents — Operate on predefined rules and immediate data only; no memory or inference (e.g., password reset bots, email autoresponders, automated sprinkler systems)
  • Model-Based Reflex Agents — Use an internal world model to infer unobserved environmental states and make more nuanced decisions; do not retain past states (e.g., network monitoring systems)
  • Goal-Based Agents — Plan sequences of actions to reach a desired outcome; include a goal state, planning mechanism, state evaluation, action selection, and world model (e.g., inventory management systems)
  • Learning Agents — Improve behavior over time through experience rather than relying solely on pre-programmed knowledge (e.g., adaptive customer service chatbots)
  • Utility-Based Agents — Handle trade-offs between competing goals without needing pre-specified priorities (e.g., flight-search agents balancing price vs. travel time)
  • Hierarchical Agents — Higher-level agents decompose complex tasks and delegate to lower-level specialized agents, which submit progress reports upward
  • Multi-Agent Systems — Combinations of the above agent types working together to achieve more complex goals

Framework 2 — Focus Categories (The Information’s Seven Types)

This framework organizes agents by the business outcome they are deployed to achieve, making it more actionable for non-technical stakeholders:

  • Business Task Agents — Handle repetitive, structured workflows such as data entry, document classification, and invoice processing; overlaps with traditional business process automation
  • Conversational Agents — Customer-facing service bots or internal support agents (IT, HR); inclusive of both external and internal use cases
  • Research Agents — Conduct information gathering and synthesis; noted as one of the first agentic experiences accessible to non-technical employees
  • Analytics Agents — Analyze structured data to produce charts, graphs, and reports
  • Developer Agents — AI coding assistants and software development tools; described as “the single most significant breakout agent so far” and the dominant theme of 2025
  • Domain-Specific (Vertical) Agents — Agents with deep specialization in a particular field such as legal, healthcare, or finance

Evidence of Current Agent Usage: Iconic Study on Agent Builders

  • A study from Iconic surveyed firms that build AI/agent software and examined how they use agents internally
  • Coding assistance is by far the most common use case at 77% of organizations
  • Other significant use cases include: content generation, knowledge retrieval, product design, and business intelligence
  • Confirms developer agents as the leading deployed agent category in practice

Framework 3 — KPMG’s TACO Framework (Simplified Functional Taxonomy)

KPMG condenses the functional breakdown into four more intuitive categories organized by task complexity, human oversight required, and system breadth:

  • Taskers — Execute well-defined individual tasks; require human in the loop
  • Automators — Manage more complex, multi-system workflows
  • Collaborators — Adaptive AI teammates managing multidimensional goals
  • Orchestrators — Coordinate multiple agents and tools to manage interdependent workflows

The speaker notes TACO’s language is more accessible to non-technical or business audiences than terms like “reflex” or “utility-based.”

The Strategic Importance of Orchestration and Multi-Agent Systems

  • Enterprises and private equity firms are actively discussing orchestrators and multi-agent systems, not just individual spot agents
  • Microsoft’s Build 2025 conference emphasized multi-agent orchestration infrastructure (e.g., Copilot Studio multi-agent orchestration) over individual premier agents
  • The Agent-to-Agent (A2A) communications protocol is emerging as infrastructure to enable agent-to-agent interaction
  • The speaker argues that thinking in systems terms — even before full deployment — is directionally correct
  • Full value from agents will come not from isolated deployments but from comprehensive digital worker organizations where agents collaborate

Infrastructure Considerations for Agent Readiness

  • Organizations should think beyond use cases and plan for the full supporting tech stack
  • Key infrastructure domains identified from the Iconic report include:
    • Model training and fine-tuning
    • LLM and AI application development
    • Monitoring and observability (near-universal requirement)
    • Inference optimization (near-universal requirement)
    • Model evaluation (near-universal requirement)
    • Model hosting
    • Data processing and feature engineering
    • Vector databases
    • Synthetic data and data augmentation
    • Coding assistance, DevOps/MLOps, product and design tooling
  • Not every organization needs all layers, but monitoring, inference optimization, and evaluation apply to nearly all

Key Concepts

  • AI Agent — An AI system that acts autonomously to accomplish tasks on behalf of a user or organization, as distinct from an assistant that responds to direct user input
  • Simple Reflex Agent — An agent that acts solely based on predefined condition-action rules and immediate inputs, with no world model or memory
  • Model-Based Reflex Agent — An agent that uses an internal world model to infer current environmental state and improve decision-making beyond immediate inputs
  • Goal-Based Agent — An agent that plans sequences of actions to reach a specified goal state
  • Learning Agent — An agent capable of improving its performance over time by learning from past interactions and experiences
  • Utility-Based Agent — An agent that selects actions by maximizing a utility function, enabling it to navigate trade-offs between competing objectives
  • Hierarchical Agent — An agentic architecture in which higher-level agents decompose tasks and delegate them to lower-level specialized agents
  • Multi-Agent System — A system in which multiple agents of various types collaborate to accomplish complex goals
  • Business Task Agent — An agent focused on automating repetitive structured business processes such as data entry or invoice handling
  • Conversational Agent — An agent designed for dialogue-based interactions, either customer-facing or internally within an organization
  • Research Agent — An agent that autonomously gathers, synthesizes, and reports on information from multiple sources
  • Analytics Agent — An agent that processes structured data to generate visualizations, reports, or insights
  • Developer Agent — An agent that assists with or autonomously performs software development tasks, including code generation and review
  • Domain-Specific (Vertical) Agent — An agent with specialized knowledge in a particular professional domain such as law, medicine, or finance
  • TACO Framework — KPMG’s four-category taxonomy of agents: Taskers, Automators, Collaborators, and Orchestrators, organized by task complexity and autonomy level
  • Orchestrator — An agent or system layer that coordinates multiple subordinate agents to accomplish complex, interdependent workflows
  • Agent-to-Agent (A2A) Protocol — An emerging communications standard enabling different AI agents to interact directly with one another within multi-agent systems
  • Inference Optimization — Techniques and infrastructure used to improve the speed and cost-efficiency of running AI model inferences at scale
  • Monitoring and Observability — Tools and practices for tracking the behavior, performance, and outputs of deployed AI agents in production

Summary

The central message of this episode is that AI agents have moved firmly from experimentation into enterprise deployment, and that practitioners need conceptual frameworks to navigate the diverse landscape of agent types in order to build effective strategies. The speaker presents and contrasts two primary organizational frameworks: a functional taxonomy (simple reflex, model-based reflex, goal-based, learning, utility-based, hierarchical, and multi-agent systems) that explains how different agents operate internally, and a focus-based taxonomy drawn from The Information (business task, conversational, research, analytics, developer, and domain-specific agents) that maps agent types to business outcomes. A simplified bridge between these perspectives is KPMG’s TACO framework. The speaker’s overarching recommendation is that organizations should resist thinking about agents as isolated point solutions and instead orient their planning toward multi-agent systems and orchestration infrastructure, as the full transformative value of agents will only be realized when they work together in coordinated, system-level architectures. Alongside choosing the right agent types for specific use cases, organizations must also invest in the surrounding technical infrastructure — particularly monitoring, inference optimization, and evaluation — to successfully operationalize agents at scale.