The news
IBM published a step-by-step technical guide to LLM agent orchestration using LangChain and its Granite models. The tutorial walks through how to build agents with persistent profiles — embedding identity, personality, and social context into the model so it can interact in a personalized, task-appropriate way. Agent profiles can be manually defined, generated by models like GPT or Granite, or aligned to specific datasets and refined dynamically through prompt engineering.
Our take
IBM publishing this as an accessible tutorial isn't the story. The story is what the tutorial reveals about where AI fluency is actually heading — and what GTM teams are about to get left behind on.
Most marketing and RevOps teams are still optimizing prompts. Write a better ChatGPT prompt, get a better output. That's fine. But agent orchestration is a different game. When you're building systems where multiple agents hand off tasks to each other — a research agent feeds a qualification agent feeds a sequencing agent — what determines whether the whole thing works isn't the model. It's the context.
That's what "profile engineering" is really about. You're not just telling an agent what to do. You're giving it a stable identity, a defined role, and the right information to make decisions autonomously across a chain of actions. Get that wrong and the agent confidently does the wrong thing at scale.
Demand AI studio has seen this pattern break in early GTM automation builds: teams stand up an agent, get impressive demo behavior, then watch it hallucinate lead stages or misread ICP signals the moment context gets thin. The fix is almost never the model. It's almost always the context layer — what information the agent has access to, in what format, and when.
The teams that are going to win with AI in 2025 and beyond aren't just good at prompting. They're building systems where context is engineered deliberately, not assumed. That's the fluency gap forming right now.
The so-what
The skill that matters next isn't prompt engineering — it's knowing how to structure context so agents can make reliable decisions without hand-holding. For GTM teams, that means getting specific about what your agents need to know: your ICP definition, your qualification criteria, your stage logic. If that information lives in someone's head instead of a documented system, no orchestration framework fixes it. The teams closing the gap aren't waiting for IT to build it for them — they're learning the patterns now, before it becomes table stakes.
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