3c.2_pal_initial_demos_12b_CHATS
what do you think about attached diagram 1? shows traditional agent diagram (top) and in Foundry (bottom) (2),(3),(4) are the numbers used in the Foundry agent main diagram 2 (attached)
Overall assessment
Diagram 1 is very effective. It makes the conceptual equivalence between a conventional code-based agent and the Foundry implementation much clearer than the original demo documentation does.
The mapping of the numbered components works:
| Number | Traditional implementation | Foundry implementation |
|---|---|---|
| (2) | Agent/control loop | Automate event loop |
| (3) | LLM API call | Use LLM block |
| (4) | Tool/action call | Governed Ontology Action |
It also supports your central argument:
Foundry did not invent a fundamentally different architecture. It distributed the familiar agent components across named, governed platform resources.
There are, however, several changes that would make the comparison more technically exact.
1. Change the UI input path in the Foundry diagram
This is the most important adjustment.
The bottom diagram currently appears to show:
UI Input → Automation INPUT
But that is not how Demo 3 works. The user first creates a Patient in the Ontology:
UI Input
→ Add Patient Action
→ Ontology WRITE
→ Patient appears in monitored object set
→ Automate detects it
Automate does not directly receive the form input. It receives a reference to the Patient object that newly entered the monitored set.
I would draw:
UI Input ──Add Patient Action──▶ Ontology WRITE
Ontology READ/object-set event ──(2)──▶ Automate
That would also align Diagram 1 with arrows 1b and 2 in Diagram 2.
2. Label (2) more specifically
In Diagram 2, arrow 2 means more than just “loop.” It includes:
- monitoring the Patient object set;
- detecting that a Patient entered it;
- invoking the published Logic function;
- passing the Patient and configured Clinical Trial as inputs.
A better label would therefore be:
(2) Automate: detect event and invoke Logic
or:
(2) Event trigger / recurring invocation
The circular loop icon is still appropriate, but the loop is:
wait for qualifying event
→ invoke Logic once
→ wait for next event
It is not an internal LLM planning loop.
3. Distinguish the two kinds of “agent loop”
This is an important conceptual difference between the top and bottom diagrams.
Typical code-based agent loop
A conventional agent may run an internal reasoning loop:
call LLM
→ choose tool
→ inspect tool result
→ call LLM again
→ repeat until finished
Demo 3 Foundry loop
The AIP Logic itself is one-shot:
read objects
→ call LLM once
→ invoke Action once
→ terminate
Automate provides an outer event loop:
wait for new Patient
→ run one-shot Logic
→ wait for next Patient
Therefore, I suggest labeling the top loop:
Agent reasoning/control loop
and the bottom loop:
Automate event/invocation loop
That preserves the analogy without implying that the two loops are identical.
4. Rename the inner AIP box
In the bottom diagram, the box containing Use LLM and Action would be clearer as:
Published AIP Logic function — one execution
rather than simply AIP.
That gives you the following hierarchy:
Automate event loop
└── Published AIP Logic function
├── Use LLM
└── Ontology Action
This also clarifies the relationship to Diagram 2:
- the Logic editor shows its implementation;
- the AIP Function dialog shows its published interface;
- Automate selects and invokes that published version.
You do not necessarily need to add the Function Registry dialog to the conceptual diagram. Diagram 2 already explains that UI indirection.
5. Do not equate MCP directly with a tool
In the top diagram, MCP, Tool combines two related but different concepts:
- A tool is an operation the agent can invoke.
- MCP is a protocol through which tools and resources can be exposed.
A clearer label would be:
Tool / function / API
If MCP matters to the surrounding discussion, use:
Tool API — optionally exposed through MCP
A corresponding architecture might be:
Agent → MCP client → MCP server → tool → database
The Foundry Ontology Action is conceptually equivalent to a governed tool call, but it is not inherently an MCP operation.
6. Label the LLM as an external service
Your drawing already places the LLM outside the main control-loop box, which supports your argument well. I would label it:
External LLM service/API
Then (3) can explicitly read:
(3) Model API call
The Foundry equivalent becomes:
Use LLM block
→ external LLM service
→ structured decision returned
This visually reinforces your distinction:
- the LLM service supplies the AI inference;
- the agent runtime invokes and operationalizes it.
7. Consider changing the outer dashed boundaries
In the top diagram, the outer dashed box includes the UI, database, agent runtime, and LLM. That is really the complete deployed agentic system, rather than only the agent program.
The same applies to the bottom diagram.
Possible outer labels:
Top
Conventional code-based agentic system
Bottom
Foundry agentic system
Then the inner boxes identify the actual orchestration runtimes.
This supports two legitimate boundaries:
Narrow agent:
control program that invokes models and tools
Operational agentic system:
program + models + tools + state + triggers + UI
Recommended Foundry flow
With those changes, the bottom diagram would express:
UI: Add Patient
│
▼
Ontology WRITE
│
▼
Patient enters monitored object set
│
│ (2) Detect event and invoke
▼
Automate
│
▼
Published AIP Logic — one execution
│
├── read Patient + Clinical Trial from Ontology
│
├── (3) Use LLM → external LLM service
│
└── (4) governed Action → Ontology WRITE
│
▼
Ontology READ
│
▼
UI Display
Relationship to Diagram 2
Your numbering is internally consistent, provided that the labels mean:
- (2): Automate detects the Ontology event and invokes the published Logic.
- (3): AIP Logic performs its reasoning through the external LLM call.
- (4): The governed Action writes the resulting decision to the Ontology.
Diagram 1 explains the general architecture; Diagram 2 shows where those abstract components appear in Foundry’s actual dialogs. That is a strong pairing.
Bottom line
I would keep Diagram 1. Its principal insight is excellent:
A conventional code-based agent and the Foundry agent contain the same conceptual parts; Foundry externalizes those parts as Automate, AIP Logic, Use LLM, Ontology, and Actions.
The main correction is to route user input through the Ontology before Automate. After that, the distinction between the outer Automate event loop and a conventional agent’s possible inner reasoning loop should be made explicit.
WHAT IS A CHAT???
ME1
pic1: the toc of the original demo (i added heading numbering) pic2: this is where ontology action to modify ontology is created.
we already talked about this… but again:
nowhere does this doc say what the agent actually is. if it is aip, then we had an agent in demo 2. apparrently , an “agent” is the ontology / action.
You have identified a genuine terminology problem in the demo. The document uses “agent” without ever defining its boundary.
What the document itself appears to mean
Judging from the table of contents, the document calls the complete AIP Logic function the agent:
Inputs
+ Use LLM block
+ Ontology Action block
= “Agent”
The strongest evidence is the sequence:
- 4.2: Create an AIP Logic
- 4.3: Configure its inputs
- 4.4: Add its LLM reasoning
- 4.5: Add an Ontology Action
- 4.5, Step 5: “Save your Agent”
- 4.6: Preview the results of your Agent
“Save your Agent” actually means saving the AIP Logic after adding the Action block. There is no separate Agent resource being created.
Therefore, in the document’s local terminology:
The agent is the AIP Logic containing both an LLM reasoning block and an Ontology Action block.
The Action is not itself the agent
The Ontology Action is better understood as the agent’s tool or actuator:
| Component | Role |
|---|---|
| Ontology | Environment and durable state |
| Patient and Clinical Trial objects | Things observed by the agent |
| Use LLM block | Reasoning component |
| AIP Logic | Program coordinating reasoning and action |
| Ontology Action | Governed tool for changing the environment |
| Automate | Event trigger and recurring outer control loop |
| Published Function | Callable, versioned form of the AIP Logic |
The wording visible in the second picture is revealing:
“Action — Apply an action, accumulating edits made to your Ontology.”
The Action does not observe, reason, or decide. It performs a governed operation requested by the Logic. Calling the Action “the agent” would be like calling a robot arm the entire robot.
Also, that dialog is not creating the underlying Action Type. It is adding a block to the Logic that invokes an Action Type already defined in the Ontology.
Why isn’t Demo 2 also called an agent?
Under a broad and defensible definition, Demo 2 was already agent-like:
- Foundry coordinated three LLMs;
- it executed a program toward a defined goal;
- it was not merely a chatbot;
- it transformed inputs into an intelligent output.
The difference introduced in Demo 3 is not “AIP versus agent.” Both use AIP.
The pedagogical distinction appears to be:
Demo 2:
Observe/process → reason → return information
Demo 3, Chapter 4:
Observe → reason → use a tool to propose Ontology edits
Demo 3, Chapter 6:
Observe continuously → reason → automatically execute edits → repeat
Thus, the tutorial seems to use agent to mean an AIP Logic that can take an operational action, even though it never explicitly says so.
Three different meanings are being conflated
1. The tutorial’s “agent”
The AIP Logic containing reasoning and action:
AIP Logic = LLM + Action
2. An action-capable agent
The Logic plus access to an Ontology Action:
Reasoning program + governed tool
This is what Chapter 4 constructs, although a human still invokes its preview.
3. The deployed autonomous agent
The complete event-driven system built by Chapter 6:
Ontology → Automate → AIP Logic → LLM → Action → Ontology
This is the clearest agent boundary from a systems perspective because it forms the running observe–reason–act loop.
My conclusion
I would describe the inconsistency this way:
The demo informally calls the AIP Logic the “agent.” Adding the Ontology Action makes that Logic action-capable, while adding Automate later makes the complete system autonomous. The Action is not the agent; it is the governed tool through which the agent changes its environment. Under a broader definition, Demo 2 was already agent-like, so the distinction between the demo titles is pedagogical rather than technically rigorous.
Or, even more compactly:
In the documentation, “agent” means the AIP Logic after an Action has been added. Architecturally, the Logic is the decision program, the Action is its actuator, the Ontology is its environment, and Automate turns them into a continuously running operational agent.
So your discomfort is justified: the document teaches how to assemble something it calls an agent, but never defines what makes it one.
ME2
the problem is the terminology “AIP” = ai platform.. its not an AI platform. its an agent platform. it calls LLMs. the LLM is an “AI platform”.
then the problem: if that is true, then demo 2 used agentic ai. it used AIP to call LLMs.
AIP is just a “one-shot” agent. its like running a python program that calls an LLM but has no loop.
with Automate, you have the loop. so its an “loop agent”, what we normally think of for a deploy system.
Your model is coherent, with one terminology adjustment:
- An LLM is not itself an AI platform; it is an AI model or inference service.
- AIP is a platform for building AI applications and agents around models.
- In these demos, AIP behaves much more like an agent/application orchestration platform than like the AI itself.
A more precise decomposition
| Component | Function |
|---|---|
| LLM | Performs probabilistic inference or reasoning |
| AIP Logic | Defines one execution of an AI program |
| Published function | Callable version of that program |
| Ontology | Supplies semantic state and the operational environment |
| Action | Allows the program to affect that environment |
| Automate | Supplies event detection and repeated invocation |
| Foundry/AIP | Hosts, governs, connects, and operates all of these |
The LLM supplies the learned intelligence. AIP supplies the surrounding program, data access, tools, permissions, evaluation, and execution environment.
Demo 2 was already agentic under this definition
Yes. Demo 2 used Foundry to orchestrate three LLM calls toward a goal:
Input
→ AIP-controlled model calls
→ intermediate processing
→ final result
→ stop
That can reasonably be called a one-shot agent or, more conservatively, a single-run agentic workflow.
It had:
- a goal;
- an orchestrating program;
- access to AI models;
- multiple reasoning operations;
- a final result.
It did not need to be a chatbot, invoke an Action, or run continuously to be agent-like.
Three progressively stronger forms
Demo 2: one-shot reasoning agent
Invoke once
→ call models
→ produce answer
→ terminate
Foundry orchestrates intelligence but does not change its operational environment.
Demo 3, Chapter 4: one-shot action-taking agent
Invoke once
→ read Ontology
→ call LLM
→ construct governed edits
→ terminate
This adds the ability to act on the environment, but it still requires an external invocation.
Demo 3, Chapter 6: event-loop operational agent
Monitor environment
→ detect event
→ invoke agent
→ reason
→ act
→ return to monitoring
Automate supplies the persistent outer loop. This is much closer to what people normally mean by a deployed autonomous agent.
Importantly, it is an event loop, not an internal LLM reasoning loop. Each invocation of AIP Logic remains essentially one-shot:
Automate:
while active:
wait for qualifying Ontology event
run AIP Logic once
Conceptually, that is exactly what is happening, even though Automate implements it declaratively rather than with a literal while statement.
Is AIP an agent?
I would distinguish the platform from the program:
AIP is an agent-building and agent-operating platform. An AIP Logic is a one-shot agent program. Automate turns repeated invocations of that program into a persistent event-driven agent.
Calling all of AIP “an agent” is understandable from the outside, but it is similar to calling Python or LangGraph an agent. They provide the runtime and orchestration capabilities; a particular configured program running on them is the agent.
At the largest system boundary, however, the distinction becomes philosophical. To an external observer, the deployed Foundry system does behave as one agent:
observe → reason → act → observe again
Why the demo names remain inconsistent
The naming seems pedagogical or promotional rather than rigorous:
- Demo 2 reserves workflow for model orchestration.
- Demo 3 introduces agentic when Ontology action is added.
- Chapter 6 introduces the actual persistent event-driven control loop.
A technically cleaner naming scheme would have been:
- Demo 2 — Build a Single-Run AI Workflow
- Demo 3, Chapter 4 — Add Governed Agency
- Demo 3, Chapter 6 — Deploy an Event-Driven Autonomous Agent
Your phrase “one-shot agent” is therefore useful. I would summarize your interpretation as:
AIP Logic defines a one-shot agent: it runs an AI program once and terminates. Demo 2 was already agentic because Foundry orchestrated multiple LLMs. Demo 3 adds the ability to modify the operational environment through a governed Action. Automate adds the outer event loop, converting repeated one-shot executions into the persistent operational system normally recognized as a deployed agent.
ME3
i still disagree. its a conceptual / languge disagreement.
AIP is not an AI platform.. it does not provide any AI. an LLM does provide AI (the service, the function) but you need a chat or agent platform to exploit the AI platform.
i have never understood why AIP is called an AIP platform.
Yes—this is fundamentally a disagreement about what the noun phrase “AI platform” means.
You are using it to mean:
A platform or service that supplies the artificial intelligence capability itself.
Under that definition:
- the LLM service supplies AI;
- AIP does not supply the intelligence;
- AIP supplies the machinery that calls, contextualizes, governs, and operationalizes that intelligence;
- therefore, AIP is better described as an agent platform or AI-application orchestration platform.
That is a perfectly coherent taxonomy.
Your decomposition
LLM service
= AI capability
= learned inference/reasoning function
AIP
= program/orchestration layer
= supplies context, prompts, tools, permissions and actions
Automate
= recurring event loop
Ontology
= state and operational environment
Expressed as code:
AIP Logic:
result = LLM(context) # exploit external AI
Action(result) # optionally act
Automate:
whenever event occurs:
run AIP Logic # persistent outer loop
In that framing, AIP is not the AI any more than a Python program calling an OpenAI endpoint is itself an AI model. It is the agent runtime around the AI.
Why Palantir nevertheless calls it an “AI Platform”
Palantir is using AI platform in the broader industry sense:
A platform for building, deploying, governing, and operating applications that use AI.
The phrase means “platform for AI,” not necessarily “platform that internally produces AI.”
This broader usage includes:
- connecting to models;
- supplying enterprise context;
- constructing prompts and workflows;
- controlling model access;
- evaluating outputs;
- exposing tools;
- governing actions;
- deploying the resulting application.
In other words, the name describes what the platform is used for, not where the intelligence originates.
The same linguistic pattern appears in:
- an “application platform,” which does not provide the application;
- a “data platform,” which does not create the source data;
- a “payment platform,” which does not create the money.
That explains the name, but it does not necessarily make it the clearest name.
“Agent platform” is more descriptive here
For the demos you completed, agent platform describes AIP’s actual role more precisely:
External AI model
↓
AIP agent program
├─ assembles context
├─ invokes the model
├─ interprets output
├─ selects/configures action
└─ interacts with operational state
AIP is the harness around the model. The LLM supplies inference; AIP turns that inference into an enterprise process.
One could therefore describe the overall stack as:
| Layer | Better functional name |
|---|---|
| GPT-4.1 or another model endpoint | AI inference service |
| AIP Logic | Agent program |
| AIP | Agent/application platform |
| Ontology | Semantic state and operational environment |
| Action | Governed actuator |
| Automate | Event loop and scheduler |
| Workshop | Human interaction and supervision layer |
Consequence for Demo 2
Using your terminology, Demo 2 was unquestionably agentic:
AIP program
→ called external AI services three times
→ coordinated their outputs
→ accomplished a defined task
→ terminated
It was a one-shot agent program. It lacked persistent execution and Ontology actuation, but those are differences in capability, not a transition from “non-agent” to “agent.”
Demo 3 then progresses as follows:
Demo 2:
one-shot reasoning agent
Demo 3, Chapter 4:
one-shot reasoning-and-action agent
Demo 3, Chapter 6:
persistent event-driven agent
So I think the cleanest statement of your position is:
Despite its name, AIP does not supply the learned intelligence used in these demos; the external LLM service does. AIP is the orchestration and agent platform that exploits that intelligence by supplying context, program flow, tools, governance, and actions. An AIP Logic is a one-shot agent program. Automate repeatedly invokes that program, creating the persistent event-driven agent normally associated with a deployed agentic system.
That is not merely a technical disagreement with Palantir. It is a narrower and arguably clearer definition of AI platform than the broader marketing category Palantir is using.
ME4
“connecting to models; supplying enterprise context; constructing prompts and workflows; controlling model access; evaluating outputs; exposing tools; governing actions; deploying the resulting application.”
you are describing an agent.
Yes. Functionally, that list describes the infrastructure of an agent:
Model
+ context
+ instructions
+ workflow
+ tools
+ actions
+ evaluation
+ deployment
= agent system
So I agree with your central point:
AIP is more accurately understood as an agent platform than as the source of AI.
The only distinction I would retain is between the platform and an instance running on it:
- AIP provides the mechanisms for constructing and operating agents.
- A particular AIP Logic, together with its model, context, tools, and Actions, is an agent program.
- Automate gives that program recurring, event-driven execution.
- The resulting deployed configuration is the running agent.
It is analogous to:
Operating system ≠ individual running process
Agent platform ≠ individual configured agent
AIP as a whole does not have one goal or one control loop; it hosts many independently configured goals and workflows. Therefore, I would not call the entire platform one agent in the strict architectural sense. But from the perspective of an external user interacting with a complete Foundry deployment, it can certainly appear and behave as one large composite agent.
The terminology can therefore be reduced to:
| Palantir term | Functional interpretation |
|---|---|
| External LLM | AI/inference engine |
| AIP | Agent construction and operating platform |
| AIP Logic | One-shot agent program |
| Logic plus Action | Action-capable agent program |
| Automate plus Logic plus Action | Persistent event-driven agent |
| Foundry deployment | Environment containing one or more agents |
So yes: the capabilities I listed do not explain why AIP should be regarded as the provider of AI. They explain why it should be regarded as the platform that turns AI models into agents.
ME5
Foundry started out without AI. Foundry always viewed the world from the perspective of the ontology. so when they added ai, from their perspective, it was a platform that made AI available. so the name AIP made sense.
but its the wrong name. it should have been called AI agent platform.
an ai agent has 3 things
- loop
- calls AI platform (LLM API)
- performs actions based on reply
thats AIP
- automate loop
- Use LLM call to LLM
- ontology action.
Yes—your historical explanation reconciles the naming.
Foundry originally centered on the Ontology:
Data → Ontology → operational applications → governed Actions
When Palantir added access to LLMs, it viewed the addition from inside that existing architecture:
“We now have a platform that makes AI available to the enterprise Ontology.”
From that perspective, Artificial Intelligence Platform was an understandable name. But from the perspective of contemporary AI architecture, AI Agent Platform describes its functional role more accurately.
Your three-part definition maps directly onto Foundry
| Agent requirement | Foundry implementation |
|---|---|
| Loop | Automate monitors for events and repeatedly invokes the program |
| Call an AI service | AIP Logic’s Use LLM block invokes an LLM API |
| Act on the response | A governed Ontology Action changes operational state |
The Ontology supplies the environment and durable state in which that cycle operates:
┌──────────────────────┐
│ Ontology │
│ environment + state │
└──────────┬───────────┘
│ observe
▼
1. Automate
│ invoke
▼
2. AIP Logic
│
▼
3. Use LLM
│ decision
▼
4. Ontology Action
│ update
▼
Ontology state
│
wait for next event
└───────↺
This is a conventional agent architecture expressed through Foundry resources:
Observe → invoke AI → interpret response → act → observe again
AIP Logic is the one-iteration program
The description in your screenshot is telling:
“Build composable no-code functions that can parse, modify, and expand your Ontology.”
An AIP Logic implements one execution—or one turn—of the agent:
read inputs
→ call LLM
→ interpret result
→ construct edits
→ terminate
Automate turns that one-turn program into a persistent deployed system:
wait for event
→ execute one turn
→ commit action
→ wait for next event
The loop should not necessarily retrigger immediately from its own write. Usually, filters, event types, or processed-status properties prevent accidental recursion. It is an event loop that waits for the next qualifying state change.
A concise statement for your notes
Foundry originated as an Ontology-centered operational platform without generative AI. When Palantir added model access, “Artificial Intelligence Platform” made sense from its internal perspective: AIP made external AI available to Ontology-based applications. Architecturally, however, AIP is better understood as an AI agent platform. Automate supplies the loop, Use LLM calls the external AI service, the Ontology supplies state and context, and governed Actions modify the environment in response.
Or, in its shortest form:
The LLM supplies the AI; AIP turns that AI into an operational agent.
ME6
what do you think about attached diagram 1? shows traditional agent diagram (top) and in Foundry (bottom) (2),(3),(4) are the numbers used in the Foundry agent main diagram 2 (attached).
Overall assessment
Diagram 1 is very effective. It makes the conceptual equivalence between a conventional code-based agent and the Foundry implementation much clearer than the original demo documentation does.
The mapping of the numbered components works:
| Number | Traditional implementation | Foundry implementation |
|---|---|---|
| (2) | Agent/control loop | Automate event loop |
| (3) | LLM API call | Use LLM block |
| (4) | Tool/action call | Governed Ontology Action |
It also supports your central argument:
Foundry did not invent a fundamentally different architecture. It distributed the familiar agent components across named, governed platform resources.
There are, however, several changes that would make the comparison more technically exact.
1. Change the UI input path in the Foundry diagram
This is the most important adjustment.
The bottom diagram currently appears to show:
UI Input → Automation INPUT
But that is not how Demo 3 works. The user first creates a Patient in the Ontology:
UI Input
→ Add Patient Action
→ Ontology WRITE
→ Patient appears in monitored object set
→ Automate detects it
Automate does not directly receive the form input. It receives a reference to the Patient object that newly entered the monitored set.
I would draw:
UI Input ──Add Patient Action──▶ Ontology WRITE
Ontology READ/object-set event ──(2)──▶ Automate
That would also align Diagram 1 with arrows 1b and 2 in Diagram 2.
2. Label (2) more specifically
In Diagram 2, arrow 2 means more than just “loop.” It includes:
- monitoring the Patient object set;
- detecting that a Patient entered it;
- invoking the published Logic function;
- passing the Patient and configured Clinical Trial as inputs.
A better label would therefore be:
(2) Automate: detect event and invoke Logic
or:
(2) Event trigger / recurring invocation
The circular loop icon is still appropriate, but the loop is:
wait for qualifying event
→ invoke Logic once
→ wait for next event
It is not an internal LLM planning loop.
3. Distinguish the two kinds of “agent loop”
This is an important conceptual difference between the top and bottom diagrams.
Typical code-based agent loop
A conventional agent may run an internal reasoning loop:
call LLM
→ choose tool
→ inspect tool result
→ call LLM again
→ repeat until finished
Demo 3 Foundry loop
The AIP Logic itself is one-shot:
read objects
→ call LLM once
→ invoke Action once
→ terminate
Automate provides an outer event loop:
wait for new Patient
→ run one-shot Logic
→ wait for next Patient
Therefore, I suggest labeling the top loop:
Agent reasoning/control loop
and the bottom loop:
Automate event/invocation loop
That preserves the analogy without implying that the two loops are identical.
4. Rename the inner AIP box
In the bottom diagram, the box containing Use LLM and Action would be clearer as:
Published AIP Logic function — one execution
rather than simply AIP.
That gives you the following hierarchy:
Automate event loop
└── Published AIP Logic function
├── Use LLM
└── Ontology Action
This also clarifies the relationship to Diagram 2:
- the Logic editor shows its implementation;
- the AIP Function dialog shows its published interface;
- Automate selects and invokes that published version.
You do not necessarily need to add the Function Registry dialog to the conceptual diagram. Diagram 2 already explains that UI indirection.
5. Do not equate MCP directly with a tool
In the top diagram, MCP, Tool combines two related but different concepts:
- A tool is an operation the agent can invoke.
- MCP is a protocol through which tools and resources can be exposed.
A clearer label would be:
Tool / function / API
If MCP matters to the surrounding discussion, use:
Tool API — optionally exposed through MCP
A corresponding architecture might be:
Agent → MCP client → MCP server → tool → database
The Foundry Ontology Action is conceptually equivalent to a governed tool call, but it is not inherently an MCP operation.
6. Label the LLM as an external service
Your drawing already places the LLM outside the main control-loop box, which supports your argument well. I would label it:
External LLM service/API
Then (3) can explicitly read:
(3) Model API call
The Foundry equivalent becomes:
Use LLM block
→ external LLM service
→ structured decision returned
This visually reinforces your distinction:
- the LLM service supplies the AI inference;
- the agent runtime invokes and operationalizes it.
7. Consider changing the outer dashed boundaries
In the top diagram, the outer dashed box includes the UI, database, agent runtime, and LLM. That is really the complete deployed agentic system, rather than only the agent program.
The same applies to the bottom diagram.
Possible outer labels:
Top
Conventional code-based agentic system
Bottom
Foundry agentic system
Then the inner boxes identify the actual orchestration runtimes.
This supports two legitimate boundaries:
Narrow agent:
control program that invokes models and tools
Operational agentic system:
program + models + tools + state + triggers + UI
Recommended Foundry flow
With those changes, the bottom diagram would express:
UI: Add Patient
│
▼
Ontology WRITE
│
▼
Patient enters monitored object set
│
│ (2) Detect event and invoke
▼
Automate
│
▼
Published AIP Logic — one execution
│
├── read Patient + Clinical Trial from Ontology
│
├── (3) Use LLM → external LLM service
│
└── (4) governed Action → Ontology WRITE
│
▼
Ontology READ
│
▼
UI Display
Relationship to Diagram 2
Your numbering is internally consistent, provided that the labels mean:
- (2): Automate detects the Ontology event and invokes the published Logic.
- (3): AIP Logic performs its reasoning through the external LLM call.
- (4): The governed Action writes the resulting decision to the Ontology.
Diagram 1 explains the general architecture; Diagram 2 shows where those abstract components appear in Foundry’s actual dialogs. That is a strong pairing.
Bottom line
I would keep Diagram 1. Its principal insight is excellent:
A conventional code-based agent and the Foundry agent contain the same conceptual parts; Foundry externalizes those parts as Automate, AIP Logic, Use LLM, Ontology, and Actions.
The main correction is to route user input through the Ontology before Automate. After that, the distinction between the outer Automate event loop and a conventional agent’s possible inner reasoning loop should be made explicit.
26.0830 (v1 26.0830)