The AI Terms That Defined 2025
Forget prompts and hallucinations—2025 belonged to orchestration, context, and new protocols.
When discussing and reading about AI, you’ll often hear about the importance of semantics. Well, as we wrap up 2025, I’ve been reflecting on the hot AI terms in a world that continues to evolve at lightning speed. I know it sounds repetitive to keep saying this, but seriously — who could have predicted that half of consumers would already be shopping using generative AI systems?
In 2025, concepts that once made the headlines—hallucinations, prompt engineering, RAG —finally reached the point where the industry stopped treating them as news. Meanwhile, different vocabulary took their place, such as MCP, orchestration, and more recently context engineering.
The Terms That Faded Into the Background
Prompt Engineering
For a brief moment, it seemed every company would hire a team of prompt engineers. There were courses, templates, and endless discussions about the “perfect” wording for LLMs.
In practice, prompt engineering folded into broader roles and tooling. Many systems optimize prompts so users don’t have to. And as the model improves, the requirement for a perfect prompt reduces. Personally, I’ve done what model providers recommend: use the LLM itself to optimize your prompt. A little bit meta, but it works!
Prompt engineering is so “2024” actually. No, seriously. Check out below the new kid in town: context engineering.
Hallucinations
When ChatGPT first landed, hallucinations were the headline risk. Every company wanted a strategy for preventing models from making things up. Most not realizing that a hallucination was often more a feature than a bug from models.
By 2025, hallucinations are still tracked and mitigated in use cases where accuracy matters more than creativity—but it’s no longer a deterrent preventing enterprises from piloting and optimizing their AI systems. They’ve become a quality metric, not a philosophical debate. Better retrieval (see below), clearer context, and more predictable model behavior does most of the work to normalize the issue. We still care—just not at conference-panel intensity.
RAG
Retrieval-Augmented Generation exploded in 2024 as companies realized they didn’t need to spend millions to fine-tune models on all their internal documents.
It’s still everywhere—it’s just no longer news. RAG became a background expectation, like ETL to move data between systems.
What Was New in 2025
At different periods of 2025, three terms really carried the energy in 2025: MCP, orchestration, and, more recently, context engineering.
MCP: A Protocol That Is Become a New Standard
MCP (Model Context Protocol) emerged as the year’s most unexpected hit.
When Anthropic introduced MCP last fall, it was a developer convenience: a clean way for models to access tools and data. MCP standardizes how agents discover tools, request context, perform actions, maintain state and preferences. The significance became clearer in 2025 as multiple vendors adopted it through the year, across the entire industry. Taking just two examples in martech are Tealium’s MCP integration announcement, followed by Braze a few months later. Instead of building one-off connectors, these servers expose capabilities to agents through a common protocol.
MCP is already inspiring variations of new “industry” protocols such as AdCP (Ad Context Protocol), which aims to define how agentic systems might operate in advertising environments. Whether these variants become standards or experiments is still unclear; let’s see how 2026 shakes out.
Orchestration: The (Most) Strategic Layer
If MCP standardizes how agents access tools, orchestration focuses on how those agents coordinate work to complete actual tasks. And in 2025, that’s where most organizations realized the real complexity lives.
Orchestration isn’t just about triggering a sequence of steps. It’s about ensuring the right agents—each with different roles, capabilities, and permissions—collaborate in a predictable way. Multi-agentic systems behave less like a single assistant and more like a small team that needs structure, handoffs, and accountability. Without orchestration, agents either duplicate effort, miss context, or make decisions out of order.
The companies getting the most value from agents will have (at least) one thing in common: a clear orchestration layer defining how work flows, who is responsible for what, and how progress is monitored.
And owning orchestration is owning control. That is why orchestration is becoming the layer every vendor wants to own. If a company controls the workflow logic between agents, it also shapes the behaviors, dependencies, and habits an organization builds over time. That’s not just a technical advantage. It’s a strategic one.
Context Engineering: The “Successor” to Prompt Engineering
If orchestration describes how agents sequence work, context engineering shapes what flows through those agents.
Anthropic’s work on context engineering crystallized the shift: instead of obsessing over the perfect prompt, you design the environment the model operates in. That includes:
What the agent knows
How history is surfaced
Which tools it can call
What rules or policies apply
The key to success moved from being able to phrase a request to being able to provide the most relevant context associated with the request. You may think that context engineering sounds similar to RAG, and it’s not completely wrong. But context engineering focuses on the system-level orchestration of all context for an agent (rather than for a model query).
This is a more durable skill. Good context design tends to survive model upgrades, provider changes, and the addition of new tools. The most successful teams won’t be the best at crafting prompts, but the best at structuring information and constraints so ordinary prompts behave predictably and reliably.
Industry Conversations Are Changing (Again)
Are you able to keep up? Stay a few days off the internet and the news, you are sure to miss something (big). In 2023, enterprises were asking vendors how they keep their data secure. That hasn’t changed. They also asked how they handle hallucinations. In 2024, it was replaced by asking how they do RAG. In 2025, they are asking what agents are available. 2026 will likely be the year when they start asking about orchestration between agents and how context is defined.
As we head into 2026, the conversation feels less about what a model can do in isolation and more about how the entire system behaves when intelligence becomes part of the architecture.
🔑 In 2026, the hardest choices won’t be about models—they’ll be about orchestration ownership, context strategy, and the frameworks you’re ready to bet on.