Plexara
What you will take away from this lesson
The words "agent" and "agentic" show up everywhere and they sound heavier than they are. The point of this lesson is to de-mystify them. You do not need to understand every aspect of modern AI to use Plexara well. You do benefit from a working mental model, the same way you benefit from a rough mental model of how a car engine works even if you never intend to fix one.
The reason this matters is communication. You would not talk to a medical doctor the same way you talk to a small child. The way an AI agent works is unusual enough that most people's default communication style does not quite fit. A short, accurate mental model fixes that.
Learning Objectives
- 01De-mystify the words "agent" and "agentic". They name a simple loop, not a kind of magic.
- 02Draw the think / call-tool / observe / think loop that defines agentic behavior.
- 03Explain, in a sentence, when an agent reaches for a tool instead of answering from what it already knows.
- 04Describe the useful mental model behind communicating with an AI agent: a college professor's breadth paired with a small child's literalism.
- 05Apply four simple communication habits that work better once that model is in place.
A professor's knowledge, a child's literalism
The single most useful mental model for communicating with an AI agent is this: the model brings the breadth and fluency of a knowledgeable professional, and it brings the memory, context, and literal-mindedness of a very small child. Not every comparison is perfect, but this one captures the specific mismatch that makes AI conversations feel different from human ones.
Once the combination is in mind, your prompts start improving on their own. You stop assuming the agent remembers what you talked about yesterday, because it does not. You stop leaving the key noun ambiguous, because the agent will not fill it in from context the way a colleague would. You stop treating confident language as evidence of accuracy. These are small adjustments, and they make a large difference in the quality of the work the agent produces.
What the AI has (professor-like)
- Vast general knowledge from training, across languages, industries, and technical domains.
- Fluent, idiomatic writing and the ability to reason step by step.
- Comfort with abstractions, analogies, and the vocabulary of almost any field.
- Confidence. Sometimes more than is earned.
What the AI lacks (child-like)
- Any memory of what you said yesterday, or last hour, unless it is in the current context.
- Any knowledge of your company, your data, or your conventions unless you supply it or wire in a tool.
- Common-sense judgment about implicit context. "That report" without a name is genuinely ambiguous.
- A native sense of when to stop. A vague prompt produces an exhaustive, confidently wrong answer just as readily as a correct one.
What an agent actually is
An agent, in the AI sense, is a language model operating inside a loop where it can take actions rather than just produce text. Those actions are called tool calls. A tool is anything the client has connected the model to: a web search, a calculator, a code executor, or an MCP server like Plexara.
"Agentic" is the adjective. An agentic workflow is one in which the model takes more than one step, usually involving at least one tool call. There is no separate "agentic AI" that is fundamentally different from a regular LLM. It is the same underlying model, run inside the loop described below.
The loop underneath everything
The loop is short enough to draw on a napkin. Most of what the AI industry means by "agentic" is this pattern, iterated as many times as needed until the model has enough information to answer.
The agent loop: think, call-tool, observe, think again
1. Your prompt arrives
The user message lands in the model along with system instructions, any operating manual (like platform_info), and whatever prior turns still fit in the context window.
2. The model reasons
The model considers what the question needs. It weighs what it already knows from training against what it would need to look up. This is the "thinking" step; some providers let you see it.
3. The model calls a tool (sometimes)
If a tool would produce a better answer than guessing, the model emits a tool call. In Plexara this is where datahub_search, trino_query, memory_recall, and the rest get invoked.
4. The tool returns an observation
The tool runs, does whatever it does, and returns a result. That result is handed back to the model as new context. The model now knows more than it did a moment ago.
5. Loop or answer
The model reasons again. If more information is needed, it calls another tool and the loop continues. Otherwise it writes the final answer to you.
That is the whole thing. You could draw it on a napkin. “Agentic” is just the adjective for software that follows this loop. It is not a separate species of intelligence.
When the agent reaches for a tool
The model does not call tools for fun. It calls them when answering the question without a tool would force it to either guess or refuse. The 104 lesson covered the three sources of knowledge a frontier model can draw from (its training data, a first-party web search, and tools wired in from the outside). The agent's decision about whether to call a tool is essentially a decision about which of those three sources the current question needs.
Where MCP fits
MCP does not change how the loop works. It changes which tools the model has to choose from. An MCP server advertises tools and resources to whichever client is connected. When the customer wires Plexara into their client, a long list of Plexara tools becomes visible to the agent, and the agent can choose to call any of them during step 3 of the loop above.
The agent is not "aware" of Plexara in some deeper sense. It simply has more tools in its list. The important implication for you: the quality of your answer often depends on whether the right tool was available to the agent, which is a governance and configuration question as much as a modeling one.
Four habits that work once the model is in place
The point of a mental model is not to collect vocabulary. It is to change how you interact with the system. Given what has been covered so far, four small communication habits consistently produce better results with an AI agent, and each maps directly back to something earlier in the lesson.
Four habits that work once you have the mental model
- 01
Name entities explicitly
Say "store 42" and "the Q3 revenue metric from the finance glossary" rather than "that store" or "the usual revenue number." The agent takes the words literally and has no separate memory of yesterday's conversation.
- 02
State your assumptions
If fiscal Q3 ends on a specific date, say so. If "active customer" has a specific definition in your business, surface it. A professor-level model with a child-level memory still needs the definition spelled out once in the current session.
- 03
Ask the agent to show its work
"List the tools you called and why" is a legitimate instruction. Tool calls are visible in most clients; asking for them to be named in the final answer surfaces whether the agent grounded its claims in your data or made them up.
- 04
Start a fresh session when the topic changes
The context window fills up and old turns compress. A fresh session is cleaner and cheaper, and Plexara memory recalls the relevant prior context without you reconstructing it (covered in 103 and 206).
Where this leads
With agents, the loop, tools, and the decision rule in place, the remaining question is how a specific MCP server fits into all of this in practice. That is what the 200 series is for, with the Plexara MCP at the center.
Key terms
Six terms cover almost every conversation about agentic behavior. "Autonomous" in particular is worth pinning down because it gets used loosely and carries connotations that do not match what agents actually do.
Key Terms
- Agent
- A language model operating inside a loop where it can call tools and react to the results. Not a separate kind of AI; a mode of operation.
- Agentic
- The adjective for software that behaves as an agent. "Agentic workflow" means a workflow in which the model takes multiple steps, often invoking tools, rather than producing a single direct answer.
- Autonomous
- Often used interchangeably with agentic, but more specific: an autonomous agent makes its own decisions about when to loop and when to stop, within bounds the client and the MCP server set. "Autonomous" does not mean "unsupervised" or "unstoppable."
- Tool call
- A structured message the model emits to invoke a tool it was told about (via MCP or a first-party integration). Looks like a function call with named arguments.
- Observationtool result
- The data the tool returns after a tool call. Gets fed back to the model as new context on the next pass through the loop.
- Agent loop
- The repeating cycle of model reasoning, optional tool call, observation of the result, and further reasoning, until the agent produces a final answer to the user.