Three-layer Architecture
1. Why Prodia Agent is better understood through three layers
Many teams initially picture an Agent as “an LLM, a prompt, and some tools”. That model is fine for lightweight demos, but it is not enough for long-running industrial use.
Manufacturing analysis is different because:
- problems are multi-turn rather than one-shot
- results must be grounded in real industrial capability and real data
- the system has to decide not only what to say, but also what to see and what it is allowed to do
That is why the current Prodia Agent is better understood through a three-layer architecture.
2. What the three layers are
| Layer | Core question | Role in Prodia |
|---|---|---|
| Harness Engineering | What execution world does the Agent run inside? | provides sessions, tools, state, events, permissions, and runtime governance |
| Context Engineering | What is this turn actually allowed to see? | decides history, memory, knowledge, time, tool boundary, and token budget |
| Prompt Engineering | How should this turn behave and respond? | defines role, rules, mode switch, output contract, and subtask boundary |
In simple terms:
- Harness builds the world
- Context selects what the turn sees
- Prompt shapes how the turn behaves
3. Why prompt alone is not enough
If prompt is expected to carry all the work by itself, several problems appear:
- history grows, but signal quality drops
- tool boundary and execution order become harder to stabilize
- one large prompt becomes responsible for role, process, context, safety, and governance all at once
- the system starts depending too much on ad hoc model behavior
That is why Prodia is no longer best framed as “prompt-first”. It is better framed as runtime plus context plus prompt working together.
4. How the three layers collaborate
More concretely:
- Harness prepares the real execution environment: session, tool access, state, and event path.
- Context decides which history, memory, glossary, knowledge, and time scope should enter the turn.
- Prompt turns those inputs into stable role behavior, output discipline, and response boundaries.
These layers do not replace one another. They complete one another.
5. What product value the three layers create
| Layer | Direct product value |
|---|---|
| Harness | makes Agent executable rather than merely conversational |
| Context | makes Agent selective and relevant rather than overloaded or forgetful |
| Prompt | makes Agent behavior stable rather than loosely improvised |
6. How the three layers relate to other Prodia mechanisms
The three-layer architecture does not replace UNS, MCP, Skills, memory, or the knowledge loop. It gives them a clearer technical place:
- UNS, MCP, and tools sit closer to the Harness side
- routing, context engineering, and memory sit closer to the Context side
- prompt assets, output contracts, and subtask constraints sit closer to the Prompt side
This makes the platform easier to extend and easier to govern as scenarios grow.
7. Where to read next
If you want to go deeper into what the Agent sees and how it behaves, continue with Context Engineering and Prompt Engineering.