Grounding research

Give each research agent a mandate, a shared research library, and access to prior work.

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

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

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

The corpus the agents reason over goes in a shared knowledge base, searched per query. Reuse one configured instance when agents share the same corpus.

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. The agent retrieves relevant profiles and analyses for each question, which keeps the full library out of the prompt.

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.

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

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

Task	Guide
Make grounded research compound	Institutional learning
End in an auditable artifact	Structured deliverable

Developer Resources

• Knowledge
• Context engineering