Research Automation Integration

Design autonomous research agents that gather intelligence while respecting enterprise guardrails. This guide extends the LangChain and AutoGen integrations with safety controls tuned for research workloads.

Architecture

1. Research Orchestrator: Manages tasks, prioritization, and result aggregation.
2. LLM Agents: Execute web searches, summarize reports, and analyze datasets.
3. AISentinel Policy Layer: Evaluates each action for compliance (PII, export controls, safe browsing).
4. Audit & Reporting: Stores decisions for later review via Auditing & Compliance.

Environment Configuration

export AISENTINEL_TENANT_ID="research-prod"
export AISENTINEL_API_KEY="<scoped-research-key>"
export AISENTINEL_RULEPACK="research-safe-browsing"
export RESEARCH_ALLOWED_DOMAINS="nist.gov,who.int"

Set up BYOK and key rotation per Encryption BYOK and Key Rotation.

LangChain Playbook

from langchain.agents import AgentExecutor
from langchain.tools import Tool
from aisentinel import Client, ApiError
import os

client = Client(
    api_key=os.environ["AISENTINEL_API_KEY"],
    tenant_id=os.environ["AISENTINEL_TENANT_ID"],
)

async def safeguarded_run(tool_input: str) -> str:
    try:
        decision = client.policies.evaluate(
            input=tool_input,
            rulepack=os.environ["AISENTINEL_RULEPACK"],
            context={"domainAllowlist": os.environ["RESEARCH_ALLOWED_DOMAINS"].split(",")},
        )
        if decision.decision == "deny":
            return f"Blocked by AISentinel: {decision.reason} (audit {decision.audit_id})"
    except ApiError as exc:
        if exc.status_code == 429:
            raise RuntimeError("Rate limit exceeded—queue request and retry later") from exc
        raise
    # Invoke original tool once policy approves
    return original_search_tool.run(tool_input)

original_search_tool = Tool.from_function(name="web_search", func=lambda q: "...")
secured_tool = Tool.from_function(name="web_search_guarded", func=safeguarded_run)

agent = AgentExecutor.from_agent_and_tools(agent=research_agent, tools=[secured_tool])

• Attach auditId to every research note for traceability.
• Store denied attempts in your knowledge base to prevent future requests.

Safety Controls

• Domain Allowlisting: Use AISentinel policies to restrict outbound fetches to vetted domains.
• Content Filters: Enable toxicity and disinformation rulepacks to prevent misuse.
• Data Loss Prevention: Combine with Remediation Automation to redact sensitive findings before publishing.

Monitoring Research Pipelines

• Stream agent events and policy decisions to your SIEM using Webhook Integrations.
• Configure dashboards that track deny rates, top blocked domains, and audit IDs.
• Schedule weekly reviews with Security and Research leads to evaluate overrides.

Error Handling

Compliance Checklist

• Maintain audit logs for six years (HIPAA) or per policy.
• Use Cryptographic Proofs when deleting research datasets containing regulated data.
• Document researcher onboarding and approvals in Team Management.

By layering AISentinel controls onto autonomous research agents, teams can explore safely while satisfying enterprise security and compliance mandates.