# Grounding research (/use-cases/deep-research/grounding-research)



Ground each research agent with three forms of context: rules in the prompt, shared source material in a knowledge base, and complete prior work in an archive. Each layer supports a distinct part of the review.

| Layer            | Holds                                                  | Mechanism                         |
| ---------------- | ------------------------------------------------------ | --------------------------------- |
| Static context   | The mandate, policy, and process shared across queries | Injected into every system prompt |
| Research library | Company profiles, sector analyses, source documents    | RAG over a vector database        |
| Prior work       | Past decisions and memos                               | File navigation tools             |

## Layer 1: static context in the prompt [#layer-1-static-context-in-the-prompt]

Place rules that apply to every question in the prompt. Load them once and inject them into every agent.

These are composition examples adapted from the [Investment Team application](https://github.com/agno-agi/investment-team). Follow its [clone, database, credentials, and research-loading setup](https://github.com/agno-agi/investment-team#quick-start) for a complete application. Its [`agents/`](https://github.com/agno-agi/investment-team/tree/main/agents), [`teams/`](https://github.com/agno-agi/investment-team/tree/main/teams), and [`workflows/`](https://github.com/agno-agi/investment-team/tree/main/workflows) define the components referenced here; [`agents/settings.py`](https://github.com/agno-agi/investment-team/blob/main/agents/settings.py) supplies shared knowledge and paths.

Import those components into your application module, or define your own before composing them. The application uses Gemini; the OpenAI variants on these pages require `uv pip install "agno[openai]"` and `OPENAI_API_KEY` as well. Shared knowledge/storage still needs the application's database and embedding-provider setup. Prose context, analyst-output variables, and local archives must be supplied by your application.

```python
from pathlib import Path

CONTEXT_DIR = Path(__file__).parent / "context"


def load_context() -> str:
    sections = [f.read_text() for f in sorted(CONTEXT_DIR.glob("*.md"))]
    return "\n\n---\n\n".join(sections)


COMMITTEE_CONTEXT = load_context()

instructions = f"""\
You are the Risk Officer on a $10M investment team.

## Committee Rules (ALWAYS FOLLOW)

{COMMITTEE_CONTEXT}

## Your Role
Enforce position limits and sector caps on every recommendation.
"""
```

Mandate, risk policy, and process are markdown files. The loader places the same text in each agent's instructions. Model instructions guide behavior; enforce position limits and other hard policy in code or tool permissions.

## Layer 2: the research library in RAG [#layer-2-the-research-library-in-rag]

The corpus the agents can search goes in a shared knowledge base. Reuse one configured instance when agents share the same corpus.

```python
from agno.agent import Agent
from agno.models.openai import OpenAIResponses

# Shared instance, imported from a settings module
from agents.settings import team_knowledge

analyst = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    knowledge=team_knowledge,
    search_knowledge=True,
    instructions="Search the research library before forming a view. Cite what you used.",
)

reply = analyst.run("What does our research say about semiconductor supply?").content
# The instruction tells the analyst to search and cite retrieved material.
```

`search_knowledge` is on by default and exposes a model-selected search tool. The instructions ask for retrieval, but do not guarantee a search on every question. For mandatory retrieval, fetch context in application code before the run and supply it to the agent.

## Layer 3: prior work on disk [#layer-3-prior-work-on-disk]

Keep complete past memos as files when a reviewer needs the original reasoning trail. Give the reading agent file tools scoped to that archive.

```python
from pathlib import Path

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.file import FileTools

memos_dir = Path(__file__).parent / "memos"

archivist = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    tools=[
        FileTools(
            base_dir=memos_dir,
            enable_save_file=False,
            enable_replace_file_chunk=False,
        )
    ],
    instructions="Check prior memos before drawing new conclusions.",
)
```

Give the memo writer write access. This archival agent receives read and search tools. Keep sensitive files outside `memos_dir` because every file under the tool's base directory may be readable.

## What each layer contributes [#what-each-layer-contributes]

| Layer   | Contribution                                   |
| ------- | ---------------------------------------------- |
| Prompt  | Instructions and context shared across queries |
| RAG     | Retrieval from a corpus too large to inline    |
| Archive | The reasoning trail behind past decisions      |

Together, the layers cover operating rules, available research, and prior decisions.

## Next steps [#next-steps]

| Task                            | Guide                                                                     |
| ------------------------------- | ------------------------------------------------------------------------- |
| Make grounded research compound | [Institutional learning](/use-cases/deep-research/institutional-learning) |
| End in an auditable artifact    | [Structured deliverable](/use-cases/deep-research/structured-deliverable) |

## Developer Resources [#developer-resources]

* [Knowledge](/knowledge/overview)
* [Context engineering](/context/overview)