Area and publish date filters for Net Search on AgentCore

When an AI agent makes use of Net Search to floor its solutions on behalf of a buyer, the group behind that agent wants area and date filters to regulate which sources the agent consults and the way recent these sources should be. A financial-services agent shouldn’t floor its solutions in an unvetted weblog. A product-information agent shouldn’t cite pricing or stock knowledge from three years in the past when the consumer requested about present availability, as inventory ranges and pricing change quickly.

At present, we’re saying runtime area and published-date filtering for Net Search on Amazon Bedrock AgentCore, a platform to construct, join, and optimize brokers at scale with any framework or mannequin. This functionality ships as a part of the web-search connector model 1.2.0. These capabilities give builders per-call management over which net domains their brokers can search and what publication-date window outcomes should fall inside, all enforced server-side. No exterior orchestration is required. When mixed with present admin-level area insurance policies, organizations have a layered filtering mannequin that enforces enterprise governance whereas giving particular person API calls the pliability to slim scope dynamically, per request.

Alongside runtime filtering, this launch additionally expands Net Search availability to 2 new AWS Areas: eu-west-1 (Dublin) and ap-northeast-1 (Tokyo). Prospects in Europe and Asia Pacific can now invoke Net Search from a regional endpoint nearer to their workloads, lowering latency and offering an EU-based entry level for organizations with knowledge proximity necessities. AgentCore makes use of a zero-egress structure the place search queries stay inside AWS. This regional enlargement offers regulated prospects in these areas a path to grounded brokers with out routing visitors throughout the Atlantic.

On this publish, we stroll by way of what runtime filtering is, why it issues, how the layered admin-plus-runtime mannequin works, methods to get began with the brand new API parameters, and what regional availability means on your workloads.

What’s new in connector model 1.2.0

This launch introduces two new capabilities throughout the filters object of the Net Search instrument enter schema:

1. Runtime area filtering

Move an embrace (allowlist) or exclude (denylist) listing of domains on each instruments/name invocation. This offers per-request management over which sources the agent might seek the advice of.

Every listing helps as much as 100 domains, counted independently.

2. Revealed-date filtering

Limit outcomes to content material revealed inside a selected date vary utilizing ISO-8601 UTC bounds:

Each filters are non-compulsory and utilized per request. Omitting them preserves the present habits the place all listed content material is eligible.

Why runtime filtering issues

Actual-world agent workloads demand extra granular management than organization-wide insurance policies alone can present:

1. Per-task supply restriction: A compliance agent analyzing regulatory updates ought to solely search .gov domains and accepted publishers, not the open net.
2. Temporal scoping: A market-intelligence agent summarizing “this week’s earnings calls” ought to by no means floor outcomes from prior quarters, even when they rank extremely for the question.
3. Dynamic enable/deny at name time: A multi-tenant platform serving completely different prospects may have completely different area insurance policies per request, with out creating separate targets for every tenant.
4. Content material freshness ensures: A buyer help agent answering “what modified within the newest launch” ought to solely return documentation revealed up to now 7 days.

Runtime filtering addresses these wants by shifting management into the API name itself.

The way it works: The request movement

The next diagram exhibits the lifecycle of a filtered search request: your agent sends a instruments/name with question and filters, the Gateway merges your runtime filters with the admin-level coverage, executes the filtered question in opposition to the net index, enforces compliance on the uncooked outcomes, and returns solely verified outcomes on your agent to floor its response on.

Determine 1: Lifecycle of a filtered Net Search request, from instruments/name to verified outcomes

The complete lifecycle is server-side. There’s no client-side filtering loop, no post-processing, and no extra roundtrips.

The layered filtering mannequin: Admin + runtime

A key design precept of this launch is that runtime filters can slim however by no means broaden the scope set by an administrator. This ensures enterprise coverage is at all times enforced, no matter what a runtime caller requests. The admin-level area lists are arrange throughout creation of the connector useful resource.

Merge logic

The next diagram illustrates how admin-level and runtime filter lists mix: embrace lists merge by intersection (solely domains current in each lists survive) whereas exclude lists merge by union (a site blocked at both stage stays blocked). Discover how d.com is dropped from the efficient allowlist as a result of it seems solely within the runtime listing, not the admin coverage.

Determine 2: How admin and runtime area lists merge, intersecting embrace lists and unioning exclude lists

Area embrace (allowlist): The efficient allowlist is the intersection of admin and runtime lists. If the admin permits [a.com, b.com, c.com] and the runtime name consists of [b.com, c.com, d.com], solely b.com and c.com are searched. The area d.com is exterior the admin area listing and is silently dropped.

Area exclude (denylist): The efficient denylist is the union of admin and runtime lists. If the admin blocks [x.com] and the runtime name excludes [y.com], each are blocked.

Notice: A runtime caller can’t search a site the admin hasn’t allowed and might’t unblock a site the admin has denied. Runtime filters can solely additional prohibit the search area.

Filter compliance habits

When filters are lively, Net Search prioritizes precision over recall. Outcomes that may’t be verified in opposition to your filter standards are excluded reasonably than returned unfiltered:

1. Area filter lively: Outcomes with no recognizable area are excluded from consideration.
2. Date filter lively: Outcomes with no acknowledged publication date are excluded.

You may obtain fewer outcomes when filters are lively, however each consequence you do obtain satisfies your specified filter standards.

Cap enforcement

There are 4 lists: admin embrace, admin exclude, runtime embrace, and runtime exclude. Every helps as much as 100 entries independently. For full configuration particulars and composition guidelines, see Configure area filtering within the Developer Information.

Getting began

Net Search is delivered by way of AgentCore Gateway, a functionality of Amazon Bedrock AgentCore that gives a managed endpoint, suitable with the Mannequin Context Protocol (MCP), for connecting brokers to instruments. Comply with these steps to allow runtime area and date filtering on your Net Search goal.

Stipulations

1. An Amazon Bedrock AgentCore Gateway with a Net Search goal pinned to connector model 1.2.0 or later.
2. AWS Id and Entry Administration (IAM) permissions: your calling agent or software wants bedrock-agentcore:InvokeGateway on the gateway Amazon Useful resource Identify (ARN), and the Gateway’s service position wants bedrock-agentcore:InvokeWebSearch. See Configure the Gateway Service Position for the whole coverage.
3. The most recent AWS SDK (Python, JavaScript, Java, .NET, Go, Ruby, or PHP).

Step 1: Create a Net Search goal with model 1.2.0

Use AWS SDK for Python (Boto3) to create (or replace) a goal pinned to model 1.2.0, with admin-level area insurance policies. If you have already got a Net Search goal on model 1.1.0, you should use UpdateGatewayTarget to pin it to model 1.2.0 as a substitute of making a brand new goal. For extra setup choices, together with the AgentCore CLI and console, see Arrange Net Search Instrument.

import boto3

gateway_client = boto3.consumer("bedrock-agentcore-control", region_name="us-east-1")

# Create a Net Search goal pinned to model 1.2.0 with admin-level area filtering
goal = gateway_client.create_gateway_target(
    gatewayIdentifier="your-gateway-id",
    title="web-search-filtered",
    targetConfiguration={
        "mcp": {
            "connector": {
                "supply": {"connectorId": "web-search", "model": "1.2.0"},
                "configurations": [
                    {
                        "name": "WebSearch",
                        "parameterValues": {
                            "domainFilter": {
                                "include": [
                                    "approved-wire-1.com",
                                    "approved-wire-2.com",
                                    "sec.gov",
                                    "investor.gov",
                                ],
                                "exclude": ["unreliable-source.net"],
                            }
                        },
                    }
                ],
            }
        }
    },
    credentialProviderConfigurations=[
        {"credentialProviderType": "GATEWAY_IAM_ROLE"}
    ],
)

print(f"Goal ID: {goal['targetId']}")
print(f"Standing: {goal['status']}")

Step 2: Invoke with runtime filters

Think about your agent screens SEC enforcement actions for a authorized workforce. The workforce solely trusts sec.gov as a major supply, and so they want actions from the present month, not historic filings. Right here’s the instruments/name payload your agent sends:

{
    "jsonrpc": "2.0",
    "id": "1",
    "methodology": "instruments/name",
    "params": {
        "title": "WebSearch",
        "arguments": {
            "question": "newest SEC enforcement actions 2026",
            "filters": {
                "domainFilter": {
                    "embrace": ["sec.gov"],
                    "exclude": []
                },
                "publishedDateFilter": {
                    "from": "2026-07-01T00:00:00Z",
                    "to": "2026-08-04T23:59:59Z"
                }
            }
        }
    }
}

The agent receives solely outcomes from sec.gov revealed within the final 5 weeks. There’s no third-party authorized commentary or outdated filings within the outcomes.

Notice: The instrument title in your instruments/name request is the title found by way of instruments/listing, which follows the sample ___WebSearch. For the goal created earlier, that’s web-search-filtered___WebSearch. The runtime embrace listing (sec.gov) intersects with the admin listing (approved-wire-1.com, approved-wire-2.com, sec.gov, investor.gov). As a result of sec.gov seems in each, it’s searched. In case you handed some-other-site.com, it might be silently dropped as a result of it’s not within the admin allowlist.

Pattern code examples

Listed below are some examples of how you should use the MCP consumer created from the AgentCore Gateway with the brand new filters at runtime. Code pattern to create MCP consumer for calling instruments

Instance 1: Medical analysis assistant with regulatory quotation necessities

You’re constructing a analysis assistant for a pharmaceutical firm’s regulatory affairs workforce. Firm coverage mandates that each reply cite solely FDA, NIH, or ClinicalTrials.gov. A quotation from a well being weblog isn’t solely a top quality concern. It’s a compliance violation that would floor in an audit. The agent must implement this on each search, no matter what question the mannequin constructs.

import json

# Pharma analysis assistant: solely accepted regulatory sources
compliance_request = json.dumps({
    "jsonrpc": "2.0",
    "id": "req-001",
    "methodology": "instruments/name",
    "params": {
        "title": "WebSearch",
        "arguments": {
            "question": "FDA drug approval course of 2026 steering",
            "maxResults": 10,
            "filters": {
                "domainFilter": {
                    "embrace": ["fda.gov", "nih.gov", "clinicaltrials.gov"]
                }
            }
        }
    }
})

# Ship by way of your MCP consumer (SigV4-signed or OAuth-authenticated)
response = mcp_client.ship(compliance_request)
outcomes = json.masses(response)

# Each result's from fda.gov, nih.gov, or clinicaltrials.gov
for end in outcomes["result"]["content"]:
    print(consequence["text"])

Each consequence your agent cites is now verifiably from an accepted regulatory supply. Even when a WebMD article ranks #1 for the question, it by no means enters the mannequin’s context window.

Instance 2: Inventory-update agent, solely this week’s protection

Your buying and selling desk runs an agent that generates inventory updates all through the buying and selling day. The issue: when a dealer asks “what’s the newest on semiconductor shares?”, the agent used to floor a highly-ranked evaluation from final quarter, and a dealer who acts on stale protection loses cash. You want each consequence to be from the previous 7 days, no exceptions.

from datetime import datetime, timedelta, timezone

# Calculate the 7-day window
now = datetime.now(timezone.utc)
seven_days_ago = now - timedelta(days=7)

stock_update_request = json.dumps({
    "jsonrpc": "2.0",
    "id": "req-002",
    "methodology": "instruments/name",
    "params": {
        "title": "WebSearch",
        "arguments": {
            "question": "semiconductor shares newest developments",
            "maxResults": 15,
            "filters": {
                "publishedDateFilter": {
                    "from": seven_days_ago.strftime("%Y-%m-%dTpercentH:%M:%SZ"),
                    "to": now.strftime("%Y-%m-%dTpercentH:%M:%SZ")
                }
            }
        }
    }
})

response = mcp_client.ship(stock_update_request)

The agent’s inventory updates now draw solely on protection revealed within the final seven days. That highly-ranked evaluation from final quarter? It by no means reaches the mannequin, so it could possibly by no means mislead a dealer.

Notice: Outcomes with no acknowledged publication date are excluded when the date filter is lively. This ensures each returned result’s verifiably inside your time window.

Instance 3: Investor-relations compliance, major sources, present quarter solely

Your investor-relations workforce makes use of an agent to arrange earnings evaluation drafts. Two onerous guidelines: (1) solely major sources: SEC filings and official investor pages: by no means crowdsourced monetary commentary websites, and (2) solely publications from this quarter, as a result of citing final quarter’s numbers in a current-quarter evaluation is a cloth error. You want each constraints enforced concurrently on each search.

# IR compliance agent: major sources + present quarter solely
financial_request = json.dumps({
    "jsonrpc": "2.0",
    "id": "req-003",
    "methodology": "instruments/name",
    "params": {
        "title": "WebSearch",
        "arguments": {
            "question": "AMZN quarterly earnings report Q2 2026",
            "maxResults": 10,
            "filters": {
                "domainFilter": {
                    "embrace": ["sec.gov", "investor.gov"],
                    "exclude": ["example-crowd-commentary-1.com", "example-crowd-commentary-2.com"]
                },
                "publishedDateFilter": {
                    "from": "2026-04-01T00:00:00Z",
                    "to": "2026-08-04T23:59:59Z"
                }
            }
        }
    }
})

response = mcp_client.ship(financial_request)

The agent’s draft now cites solely official filings and wire studies from Q2 and Q3 2026. Crowdsourced commentary and opinion items are blocked even when they match the question completely. Something from prior quarters is excluded no matter supply.

Instance 4: SaaS platform with per-tenant supply insurance policies

You use a research-agent SaaS platform serving healthcare, authorized, and financial-services prospects. Every tenant has contractually agreed supply insurance policies. Your healthcare buyer’s Enterprise Affiliate Settlement (BAA) requires citations from PubMed, CDC, and accepted medical analysis databases solely. Your authorized buyer insists on Cornell LII and official courtroom data. Your finance buyer mandates SEC, the Federal Reserve, and accepted wire providers. You possibly can’t spin up a separate gateway goal per tenant (you have got 200 tenants and counting), so that you apply tenant-specific filters at runtime utilizing the Strands Brokers SDK.

from strands import Agent
from strands.fashions import BedrockModel
from strands.instruments.mcp.mcp_client import MCPClient
from mcp.consumer.streamable_http import streamablehttp_client

# Per-tenant area insurance policies (out of your config database)
TENANT_POLICIES = {
    "tenant-healthcare": {
        "embrace": ["pubmed.ncbi.nlm.nih.gov", "who.int", "cdc.gov"],
        "exclude": ["example-consumer-health-site.com"],
    },
    "tenant-legal": {
        "embrace": ["law.cornell.edu", "supremecourt.gov", "uscourts.gov"],
        "exclude": [],
    },
    "tenant-finance": {
        "embrace": ["sec.gov", "federalreserve.gov", "example-approved-wire.com"],
        "exclude": ["example-social-forum-site.com"],
    },
}


def search_for_tenant(tenant_id: str, question: str, days_back: int = 30):
    """Execute a policy-compliant net seek for a selected tenant."""
    from datetime import datetime, timedelta, timezone

    coverage = TENANT_POLICIES[tenant_id]
    now = datetime.now(timezone.utc)
    date_from = (now - timedelta(days=days_back)).strftime("%Y-%m-%dTpercentH:%M:%SZ")

    def create_transport():
        return streamablehttp_client(
            gateway_url,
            headers={"Authorization": f"Bearer {get_token()}"},
        )

    mcp_client = MCPClient(create_transport)

    mannequin = BedrockModel(
        model_id="us.anthropic.claude-sonnet-4-20250514-v1:0",
        max_tokens=2048,
    )

    with mcp_client:
        instruments = mcp_client.list_tools_sync()
        agent = Agent(mannequin=mannequin, instruments=instruments)

        # The agent's system immediate instructs it to cross these filters
        # on each WebSearch invocation
        system_prompt = f"""You're a analysis assistant. When utilizing WebSearch,
ALWAYS embrace these filters in your instrument name:

- domainFilter.embrace: {coverage['include']}
- domainFilter.exclude: {coverage['exclude']}
- publishedDateFilter.from: {date_from}

By no means search exterior accepted domains."""

        consequence = agent(question, system_prompt=system_prompt)
        return consequence.message


# Utilization
reply = search_for_tenant(
    "tenant-healthcare",
    "What are the newest scientific trial outcomes for GLP-1 medication?"
)

One gateway goal serves 200 tenants, every with their very own supply coverage, enforced dynamically at name time. Your healthcare tenant’s agent won’t ever cite Reddit or WebMD, and your finance tenant’s agent won’t ever cite a weblog publish, despite the fact that they share the identical underlying infrastructure.

Necessary: System-prompt-based filtering (as proven earlier) depends on the agent following directions. It isn’t onerous enforcement. For onerous enforcement, configure allowed domains on the goal stage (admin embrace listing). Runtime filters handed instantly within the instruments/name payload present API-level enforcement that can’t be bypassed by the mannequin.

Enter schema reference

The filters object launched in connector model 1.2.0 provides domainFilter (embrace/exclude lists, as much as 100 domains every) and publishedDateFilter (inclusive ISO-8601 UTC bounds) alongside the present question and maxResults fields. For the whole enter schema and field-by-field reference, see Enter schema within the Amazon Bedrock AgentCore Developer Information.

Availability and 0 knowledge egress

Runtime area and date filtering is offered as we speak within the US East (N. Virginia) (us-east-1), Europe (Eire) (eu-west-1), and Asia Pacific (Tokyo) (ap-northeast-1) Areas for Net Search on Amazon Bedrock AgentCore.

Net Search advantages from the zero knowledge egress structure of AgentCore: search queries are served totally inside AWS infrastructure. Buyer queries should not despatched to a third-party search engine or routed exterior AWS. For organizations in regulated industries, akin to monetary providers, healthcare, and authorities, this removes a complete class of compliance evaluate.

Backward compatibility

These options are absolutely backward suitable:

1. No breaking modifications. Model 1.2.0 is a minor launch. The brand new fields are additive and the filters object is non-compulsory. Present API calls with out filters proceed to work precisely as earlier than.
2. SDK help. Out there throughout AWS SDKs (Python (Boto3), JavaScript/TypeScript, Java, .NET, Go, Ruby, and PHP SDKs), AWS Command Line Interface (AWS CLI) and AgentCore CLI.
3. Console help. The AWS Console for AgentCore Net Search targets now surfaces each embrace and exclude domain-list inputs within the connector configuration UI.

Conclusion

Runtime area and published-date filtering give builders constructing on Amazon Bedrock AgentCore Gateway the per-call precision they should construct reliable, policy-compliant brokers with out sacrificing the simplicity of a managed, server-side search instrument.

Whether or not you’re limiting an agent to accepted regulatory sources, scoping outcomes to the final 24 hours, or dynamically making use of tenant-specific insurance policies, Net Search on AgentCore retains you in management with zero knowledge egress, IAM-native authentication, and no exterior dependencies.

These options can be found as we speak in US East (N. Virginia) (us-east-1), Europe (Eire) (eu-west-1), and Asia Pacific (Tokyo) (ap-northeast-1) AWS Areas for Net Search on Amazon Bedrock AgentCore.

Get began as we speak:

Concerning the authors