# LLM Evolution Landscape: From Chat to Agentic Orchestration

## Executive Summary
The Large Language Model (LLM) landscape is undergoing a fundamental paradigm shift from **passive conversation** (Chat) to **active goal-oriented execution** (Agentic AI). While the initial era of LLMs (ChatGPT, Claude) focused on high-quality text completion and reasoning, the current frontier is defined by "agentic" behaviors: planning, tool use, reflection, and multi-agent collaboration. This evolution is supported by nascent standards—most notably the **Model Context Protocol (MCP)**—and is being industrialised by enterprise platforms like **MuleSoft, Workato, and Glean**, which are moving from point-to-point integration toward "Agentic Orchestration."

## 1. The Historical Trajectory: From Chat to Agentic AI
The transition in LLM capabilities can be viewed as a progression of complexity in how models process "intent" vs. "execution."

### The Eras of LLM Development
- **Chat & Completion Era (2020s):** Models like early ChatGPT and Claude functioned as sophisticated "Prompt $\rightarrow$ Response" systems. The primary value was language understanding and conversational fluency [1, 2].
- **Reasoning & Long Context (2023–2024):** The introduction of models capable of structured reasoning (e.g., Claude's reasoning capabilities) and massive context windows shifted the focus toward handling complex, multi-part queries in a single turn [3].
- **The Agentic Era (Present):** The current paradigm shift defines "Agentic AI" as the *methodology* of planning, decision-making, and adaptation, while "AI Agents" are the *implementations*. Instead of a single prompt-response cycle, these systems operate on a **"Goal $\rightarrow$ Plan $\rightarrow$ Execution $\rightarrow$ Reflection"** loop [4, 5].

### Technical Milestones
- **Reasoning Paradigms**: Development of Chain-of-Thought (CoT), Tree of Thoughts (ToT), and Plan-and-Solve structures has provided the scaffolding for models to "think" before they "act" [6, 7].
- **Acting & Tool Use**: The **ReAct** (Reasoning and Acting) pattern is a pivotal milestone, interleaving reasoning traces with task-specific actions, allowing a model to adjust its plan based on the feedback from a tool call [8, 9].
- **Multi-Step Orchestration**: The focus is shifting from binary tool selection (choosing one tool) to **dependency-aware combinatorial decision-making** (chaining multiple tools with specific outputs as inputs for the next) [10].

## 2. Agentic Tools & High-Utility Applications
Modern AI applications are moving beyond general-purpose chat boxes toward specialized "thinking partners" and "worker agents."

### Google: Gemini Deep Research & NotebookLM
- **Gemini Deep Research**: An autonomous agentic feature designed for long-horizon research. It can browse hundreds of websites, perform multi-step synthesis, and now supports the **Model Context Protocol (MCP)** to connect to external data and tools [11, 12].
- **NotebookLM**: A "source-grounded" research tool. It differs from general chat by anchoring its reasoning in a user-provided dataset (PDFs, notebooks), focusing on internal information synthesis rather than external browsing [13].

### Anthropic: Claude Code & Claude Cowork
- **Claude Code**: A developer-centric CLI tool. It is an agent that lives in the terminal, capable of performing file edits, running shell commands, and managing complex development workflows like bug fixes [14, 15].
- **Claude Cowork**: A knowledge worker-centric desktop application. It handles multi-step autonomous tasks (e.g., "write a research paper" or "organize these files") by interacting with local files and third-party apps like Slack and Google Workspace [16, 17].

### Emerging Trends
- **OpenClaw**: An open-source personal AI assistant designed to run locally on a user's device, acting as a gateway to various chat platforms (Discord, Telegram) [18].
- **OpenCodeClaw**: A mobile-first relay specifically for controlling "opencode" AI coding agents via Telegram [19].

## 3. The Infrastructure of Agents: Standards & Protocols
The lack of standardization was a major hurdle for agentic AI. New protocols are emerging to create an interoperable "Agentic Web."

### Model Context Protocol (MCP)
Introduced by Anthropic, **MCP** is an open standard for connecting LLM applications to external data sources and tools [20].
- **Mechanism**: Based on JSON-RPC 2.0, it provides a "plug-and-play" interface for **Resources** (data), **Prompts** (templates), and **Tools** (actions) [21].
- **Impact**: It allows developers to build a tool server once and have it accessible to any LLM host (e.g., Gemini, Claude, etc.), mimicking the success of the Language Server Protocol (LSP) [21].

### Emerging Frameworks
Beyond MCP, various frameworks and concepts are emerging to address the multi-agent and capability-sharing space:
- **Agent Skills**: A conceptual format for packaging specialized capabilities. A "skill" is a directory containing instructions and scripts, allowing agents to identify and "equip" specific capabilities via metadata [22, 23].
- **Agent-to-Agent (A2A) & ACP**: Proposals such as A2A and the Agent Communication Protocol (ACP) focus on how independent agents can negotiate, discover, and communicate with each other [24, 25].
- **Open Agent Protocol (OAP)**: A proposal for a unifying specification addressing the "governance" layer—billing, accountability, and legal interfaces for agent services [26].

## 4. Enterprise Integration: From Integration to Orchestration
Enterprise platforms are pivoting from "Point-to-Point Integration" (connecting App A to App B) to "Agentic Orchestration" (providing a fabric where agents can navigate the enterprise).

### The Platform Landscape
- **MuleSoft**: Positioning itself as an **Agent Fabric**. It provides an **AI Gateway** for centralized LLM routing and security, and a **Bridge** to expose production APIs as agent-usable tools while maintaining existing governance [27, 29, 30].
- **Workato**: Introducing **Agent Studio** and **Genies**. Genies are goal-oriented agents that use "Skills" (modular workflows) to execute multi-step business processes (Recipes) [31, 33, 34]. Workato's **Enterprise MCP** ensures that these agentic actions remain governed and auditable [32].
- **Glean**: Acting as the **System of Context**. Glean focuses on **Agentic RAG**, using agents to perform "smarter retrieval" across fragmented enterprise data [35]. It builds an **Enterprise Graph** to provide the memory and relational context agents need to perform "real work" [36].

## Conclusion: The "Agentic" Synthesis
The shift from ChatGPT to "Agentic AI" is not just a change in UI; it is a transition from **probabilistic text generation** to **deterministic goal execution**. By combining **Reasoning Paradigms** (ReAct, CoT), **Standardized Protocols** (MCP), and **Enterprise Fabrics** (MuleSoft, Workato), the industry is moving toward a world where AI agents are no longer just "talking" about work—they are autonomously navigating, planning, and executing it.

## Open Questions
- How will the "Agentic Web" handle multi-agent conflict resolution and billing in decentralized environments?
- What are the latency implications of multi-step "Plan $\rightarrow$ Act $\rightarrow$ Reflect" loops in real-time enterprise environments?
- How will permission-aware retrieval in tools like Glean evolve as agents become more autonomous in data discovery?

## Sources
1. Agentic AI & AI Agents: A Chronological Research Timeline — https://agentichistory.org/history.html
2. Agentic Large Language Models, a survey — https://arxiv.org/html/2503.23037v3
3. Multi-Step Reasoning with Large Language Models, a Survey — https://dl.acm.org/doi/full/10.1145/3774896
4. \model: Synergizing Reasoning and Acting in Language Models — https://arxiv.org/pdf/2210.03629v3
5. Demystifying Chains, Trees, and Graphs of Thoughts — https://arxiv.org/html/2401.14295v3
6. Agentic AI vs. AI Agents: Differences & What You Need to Know — https://www.joinleland.com/library/a/agentic-ai-vs-ai-agents
7. Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM — https://p.rst.im/q/aclanthology.org/2025.acl-long.605.pdf
8. The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration — https://arxiv.org/html/2603.22862v1
9. Agentic AI: The age of reasoning—A review — https://www.sciencedirect.com/science/article/pii/S2949855425000516
10. The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration — https://arxiv.org/html/2603.22862v1
11. Gemini Deep Research - あなたのニーズに応えるリサーチ アシスタント — https://gemini.google/overview/deep-research/
12. 1. Start the research task — https://ai.google.dev/gemini-api/docs/deep-research
13. Understand Anything — https://notebooklm.google/
14. Claude Code by Anthropic | AI Coding Agent, Terminal, IDE — https://www.anthropic.com/product/claude-code
15. Claude Code by Anthropic | AI Coding Agent, Terminal, IDE — https://www.anthropic.com/product/claude-code
16. Claude Cowork | Claude by Anthropic — https://claude.com/product/cowork/
17. Choosing between Claude Cowork or Chat | Claude by Anthropic — https://claude.com/resources/tutorials/choosing-between-claude-cowork-or-chat
18. OpenClaw — Personal AI Assistant — https://openclaw.ai/
19. MichaelG0501/OpencodeClaw — https://github.com/MichaelG0501/OpencodeClaw
20. Introducing the Model Context Protocol — https://www.anthropic.com/news/model-context-protocol
21. Specification - Model Context Protocol — https://modelcontextprotocol.io/specification/2025-11-25
22. Agent Skills Overview - Agent Skills — https://agentskills.io/home
23. Specification - Agent Skills — https://agentskills.io/specification
24. Overview - A2A Protocol — https://a2a-protocol.org/latest/specification/
25. Agent Communication Protocol (ACP) (IBM Research) — https://research.ibm.com/projects/agent-communication-protocol
26. openagentprotocol-OAP/oap-spec - spec/v1.0/OAP-CORE-1.0.md — https://github.com/openagentprotocol-OAP/oap-spec/blob/main/spec/v1.0/OAP-CORE-1.0.md
27. Agents everywhere. Managed under one control plane. — https://www.mulesoft.com/ai/agent-fabric
28. Agentforce Connector 1.3 | MuleSoft Documentation — https://docs.mulesoft.com/agentforce-connector/latest/
29. AI Gateway Platform — https://www.mulesoft.com/platform/ai-gateway
30. Introducing MuleSoft’s AI Gateway LLM Capabilities | MuleSoft Blog — https://blogs.mulesoft.com/news/ai-gateway-llm-capabilities/
31. Agent Studio - Agent orchestration — https://docs.workato.com/en/agentic/agent-studio/agent-orchestration
32. A Guide to Workato Enterprise MCP: Secure, Scalable Agentic Integration for the Enterprise — https://www.workato.com/the-connector/enterprise-mcp-guide/
33. Skills | Workato docs — https://docs.workato.com/en/agentic/skills
34. AI model and job description — https://docs.workato.com/en/agentic/agent-studio/ai-model/ai-model
35. Agentic RAG explained: Smarter retrieval with AI agents — https://www.glean.com/blog/agentic-rag-explained
36. Workplace Search AI – Instantly Find Answers Across All Apps — https://www.glean.com/product/workplace-search-ai