Artificial intelligence is rapidly moving beyond generating witty text or insightful summaries. The focus is shifting towards building agents – AI systems capable of reasoning, planning, and taking actions in the real world by interacting with tools and data. This evolution is fundamentally tied to how we communicate with and guide these models, a practice known as prompt engineering.

The journey of prompting AI agents has been a fascinating progression, moving from ingenious text-based 'hacks' to sophisticated, standardized protocols. Let's trace this path through some key milestones.

Stage 1: Early Attempts - The Rise of Reason and Act (ReAct)

In the early days of building agents with large language models (LLMs), developers relied heavily on carefully crafted prompts to structure the model's thinking and actions. A standout technique from this era was ReAct (Reason + Act).

ReAct introduced a structured prompting pattern that mirrored a human thought process. The prompt would guide the model to first generate a Thought: – essentially an internal monologue where the AI reasoned about the problem and the next logical step. This was immediately followed by an Action: – an instruction for the model to invoke an external tool or API, often specifying parameters within the text itself.

The Observation: resulting from the action would then be fed back into the prompt, allowing the model to continue its sequence of Thought: and Action:.

• How it worked: The prompt explicitly instructed the model to alternate between thinking and acting steps, requiring precise text formatting for the external system to parse and execute the actions.
• Significance: ReAct proved that LLMs could perform multi-step tasks involving external tools by explicitly structuring their internal process and external interactions within the prompt.
• Limitations: This method was inherently brittle. It relied on the model generating text in a very specific, parsable format. Any deviation could break the action execution flow, requiring complex and often fragile parsing mechanisms.

Stage 2: Standardizing Interaction - OpenAI's Function Calling / Tools

A major step forward in agent development arrived with models designed to handle structured tool definitions more reliably. OpenAI's Function Calling (later generalized as Tools) marked a significant shift away from purely text-based action prompts.

Instead of embedding tool descriptions and call formats within the main prompt text, developers could define available tools with structured schemas (like JSON) for their parameters. The model, specifically trained for this purpose, could then analyze the user query or its internal state and respond with a structured output (e.g., a JSON object) indicating its intention to call a specific function with specified arguments.

• How it worked: Developers provide tool descriptions via a dedicated API parameter. The model's response format includes a specific mode for declaring tool calls as structured data, separate from natural language responses.
• Significance: Function calling made integrating LLMs with APIs far more robust and reliable. It standardized the interface between the model's decision-making and the execution environment, reducing reliance on error-prone text parsing. This enabled more reliable and scalable agentic applications.
• Evolution: OpenAI has continued to refine this, training newer models to leverage tools via dedicated API fields, making it the recommended way to enable tool use over manual prompt injection. The OpenAI Agents SDK further builds on this for complex workflows.

Stage 3: Enhancing Context and Data Access - Anthropic's Model Context Protocol (MCP)

While OpenAI's tools focused on enabling agents to act on the world, Anthropic has been driving innovation in how agents understand and ground themselves in the world's information. Anthropic's Model Context Protocol (MCP) addresses the critical need to provide agents with reliable, up-to-date, and structured context from external data sources.

MCP is designed as a standard for connecting AI assistants to diverse systems like databases, document repositories, CRMs, and more. It's about providing the agent with the necessary background knowledge and current state information in a structured, controlled, and secure manner.

• How it worked: MCP proposes a standardized way for systems to deliver relevant data to the AI model, ensuring the information is accurate, timely, and respects access permissions. It focuses on providing the 'knowledge' needed to inform decisions and responses.

• Significance: MCP represents an evolution in providing rich, dynamic context. While tool use is about doing, MCP is about knowing – accessing the information required to decide what to do or what to say. By standardizing data access, it aims to make agents more knowledgeable, accurate, and capable of tasks requiring information synthesis from multiple sources.

• Relationship to Tools: MCP is highly complementary to tool use. An agent might use MCP to retrieve customer history (context) and then use a tool (via function calling) to update their account based on that history and the user's request.

Conclusion: Towards More Capable and Grounded Agents

The evolution of prompt engineering for AI agents highlights a clear trend: a move from clever text-based patterns to structured, reliable, and integrated approaches. We've progressed from manually guiding the model's internal monologue (ReAct) to providing standardized interfaces for action (OpenAI Tools) and developing protocols for accessing rich, external context (Anthropic MCP).

These advancements are paving the way for significantly more sophisticated AI agents – systems that can not only reason and act but also stay informed, access relevant real-world data, and operate more reliably and effectively. This ongoing evolution is crucial for building the next generation of capable and trustworthy AI applications.