Where we are in 2025–26
The numbers that frame every board conversation about agentic AI right now.
What is an agent, precisely?
An agent is a system that perceives its environment, reasons about it, takes action, and stores what it learns. Four components — each one necessary.
Perception
The agent takes in inputs from its environment — structured data, unstructured text, documents, API responses, even images. Unlike RPA, it can process ambiguous or incomplete inputs.
Reading an unstructured supplier email and extracting contract terms
Reasoning
The agent uses an LLM to reason about the current state, available tools, and goal — deciding what action to take next. This is the core capability RPA lacks.
Determining whether a contract clause violates company policy
Action
The agent executes — calling APIs, writing to databases, triggering workflows, querying knowledge bases, or delegating to sub-agents. Powered by a tool registry.
Flagging the clause, drafting a revision, and routing to legal review
Memory
Three types: session (current conversation), episodic (past interactions), semantic (domain knowledge base). Memory makes agents consistent, context-aware, and improvable.
Remembering this vendor's past negotiation patterns when drafting the counter-offer
RPA vs Agentic AI — the 8-dimension comparison
Understanding the architectural shift that determines which problems each approach can solve.
| Dimension | Traditional RPA | Agentic AI |
|---|---|---|
| Decision Logic | Deterministic: if X → do Y, always | Goal-directed: reason toward outcome, path varies |
| Data Handling | Structured only (forms, tables, APIs) | Structured + unstructured (docs, emails, images) |
| Exception Handling | Breaks and escalates to human | Reasons through novel situations, learns from outcomes |
| Integration | UI-layer bots, brittle on UI change | API-native, multi-system dynamic orchestration |
| Learning | Static — manual rule updates required | Adaptive — improves from feedback over time |
| Scalability | Linear: more processes = more bots | Network effects: agents improve each other |
| Human Oversight | Constant intervention required | Intelligent handoffs with full context transfer |
| Best For | High-volume, stable, rule-based tasks | Complex, variable, multi-step decision workflows |
Andrew Ng's 4 Agentic Design Patterns
The canonical framework for building enterprise agents — from DeepLearning.AI. Each pattern maps to a class of business problems.
Reflection
The agent critiques and revises its own output before returning it.
A code review agent that writes code, then evaluates it against security and performance standards before submitting.
Dramatically reduces error rates in output-heavy workflows.
Tool Use
The agent calls external APIs, databases, calculators, and search engines to augment its reasoning with real-world data.
A finance agent that pulls live exchange rates, queries ERP data, and calculates currency exposure — all in one reasoning chain.
Enables agents to work on live, dynamic data instead of training knowledge cutoffs.
Planning
The agent decomposes a high-level goal into sub-tasks, executes them in sequence or parallel, and tracks progress toward the goal.
An onboarding agent that creates the user account, provisions permissions, schedules training, and sends a welcome package — across five systems.
Unlocks multi-step, cross-system workflows that traditional automation can't handle.
Multi-Agent Collaboration
Specialized agents work in concert — a researcher agent gathers information, an analyst processes it, a writer produces the output, a reviewer critiques it.
A market intelligence system where one agent monitors competitor pricing, another models impact on margins, a third drafts the weekly report.
Parallelization, specialization, and redundancy — achieving quality and speed simultaneously.
Which framework for which problem?
Framework selection is an architectural decision — choosing based on hype rather than workflow characteristics is one of the most common ways agentic projects fail.
LangGraph
Production-readyStateful, long-running enterprise workflows
Graph-based agent framework where nodes are processing steps and edges define flow. Excellent for workflows that need explicit state management, human-in-the-loop checkpoints, and production-grade error recovery.
CrewAI
Production-readyRole-based multi-agent teams
Agents are assigned roles (Researcher, Analyst, Writer) and collaborate on tasks. Fastest adoption curve; 60% of Fortune 500 reported using CrewAI as of 2025. Best for knowledge work automation with clear role specialization.
AutoGen (Microsoft)
Production-readyConversational multi-agent orchestration
Agents coordinate through structured message exchange — one agent can trigger another based on conversation content. Now merged with Semantic Kernel into Microsoft's unified Agent Framework.
Model Context Protocol (MCP)
Emerging standardStandardized agent-to-tool integration
Anthropic's open standard for connecting agents to data sources and tools. Rapidly becoming the integration backbone — allowing agents to connect to any MCP-compatible service without custom connectors.
Ready to go deeper?
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