We're building a gateway that lets AI agents talk to any API using plain English. No more hand-rolling auth, pagination, or retries for every single service.
from sag import Gateway
result = Gateway().query("Get revenue from Stripe")
# → { revenue: "$142,580", growth: "+44.9%" }Natural language in, structured data out. Here's a typical gateway session.
You've built a great agent. Now you need it to talk to Stripe, Salesforce, your database, and six other services. Each one is a separate project. O(N × M) integrations don't scale.
Each agent-service pair has its own auth flow, error handling, and data transform. When Stripe changes their API version, you're fixing it across every agent that touches payments.
Pagination logic, retry with backoff, credential rotation, rate limit handling — none of this is what makes your agent valuable. But it eats 40-60% of dev time.
When an agent action fails, you're tracing logs across multiple services with no shared context. There's no unified view of what happened, when, or why.
Your agent says what it needs in plain language. The gateway figures out which service to call, handles auth, pagination, and retries, then returns clean typed data. That's it.
Powered by frontier LLMs. Takes a natural language request, figures out the intent, pulls out the parameters, and resolves which service and endpoint to hit.
Picks the right service, constructs the API call, grabs creds from the vault, handles pagination and rate limits. For multi-service queries, it fans out in parallel and merges the results.
Every API returns data differently. The normalizer takes whatever comes back and transforms it into clean, typed structs your agent can actually work with. No more parsing surprises.
Your agents on the left, your services on the right, and us in the middle.
This is what we're trying to kill. Hundreds of lines of plumbing replaced with one call.
import stripe, os
from datetime import datetime, timedelta
stripe.api_key = os.environ["STRIPE_KEY"]
now = datetime.now()
start = now - timedelta(days=30)
# Fetch current period
current = stripe.Charge.list(
created={
"gte": int(start.timestamp()),
"lte": int(now.timestamp())
}, limit=100)
pages = list(current.auto_paging_iter())
# Fetch comparison period (YoY)
# Handle pagination, retries...
# Normalize response format...
# Calculate growth metrics...
# ~30 more lines of boilerplatefrom sag import Gateway
gw = Gateway(region="us-east-1")
result = gw.query(
"Get last 30d revenue from Stripe, "
"compare YoY"
)
print(result.current.revenue) # $142,580
print(result.yoy_growth) # +44.9%Python, TypeScript, and Go — designed to be a one-liner to get started. REST API for everything else.
from sag import Gateway
gw = Gateway(
api_key="sk_live_••••7x9K",
region="us-east-1",
timeout=30,
)
# Single natural-language query
result = gw.query(
"Get last 30d revenue from Stripe,"
"break down by product line"
)
print(result.total_revenue) # $142,580
print(result.breakdown) # [{...}]A few examples of what's possible when agents can reach any service.
"Get last month's revenue from Stripe and reconcile against QuickBooks" — one query instead of two APIs and a spreadsheet.
Your ops agent can spin up EC2 instances, pull CloudWatch metrics, or manage IAM roles — all through natural language. Works with AWS, GCP, Azure.
Pull Salesforce pipeline data, update HubSpot deals, check Marketo campaign stats — your sales agent gets a single interface to all of it.
Pull CrowdStrike alerts, check Okta policies, query Splunk — without maintaining three separate integrations. Cross-platform security without the glue.
Run Snowflake queries, kick off dbt jobs, check Airflow DAG status. Your data agent manages the whole pipeline through conversation.
Deploy with ArgoCD, read GitHub Actions logs, scale Kubernetes pods — your DevOps agent talks to your infra the same way you'd talk to a colleague.
Honest comparison with the alternatives.
| Capability | Direct API | Aggregators | SAG |
|---|---|---|---|
| Setup time / service | Weeks | Days | Minutes |
| Natural language input | ✗ | ✗ | ✓ |
| Auto-authentication | ✗ | partial | ✓ |
| Pagination handling | ✗ | partial | ✓ |
| Retry + backoff | ✗ | basic | ✓ |
| Response normalization | ✗ | partial | ✓ |
| Multi-service fan-out | ✗ | ✗ | ✓ |
| Agent-native SDK | ✗ | ✗ | ✓ |
| Unified audit log | ✗ | partial | ✓ |
We know agents touching prod services is scary. Here's how we're handling it.
Everything encrypted in transit and at rest. Credentials never stored in plaintext.
Audit is in progress — we're designing around AICPA Trust Services Criteria from the start, not bolting it on later.
Fine-grained access control. Define exactly which agents can access which services, down to the operation level.
Every request, routing decision, and response gets logged to an append-only store. Tamper-evident checksums included.
Multi-AZ deployment targeting 99.99% uptime. Automated failover so one zone going down doesn't take your agents with it.
For sensitive workloads — private connectivity to your infra without traversing the public internet.
Secrets Manager-backed credential storage with auto-rotation. Your API keys never touch our application layer.
Enterprise plans get a named account engineer. We're a small team right now, so you'll actually talk to the people building this.
We'll walk you through the gateway, show you how it connects to your stack, and answer any questions. No sales pitch — just a technical conversation.
Or just email us at contact@selectedagenticgateway.com