Production AI That Actually Works

ValGuard adds built-in deterministic validation, orchestration, observability, and cost control to every step of your multi-agent workflows — in microseconds, by default.

How it works

Supervisor → Agents

Routes the request

Runs each step

Hands off between agents

ValGuard

One layer, four systems

Orchestration
Routes agents, branches, tool calls
Validation
Schema + policy checks, every step
Cost Control
Budgets, caps, cheap-first fallback
Observability
Logs, audit trail, drift alerts

Tools

APIs, CRM, search

External systems

Model calls

One layer. Four systems. No more gluing tools together.

Shadow mode first One URL No model changes Live in minutes

Request Demo Explore API integrations

Measured speed, not marketing claims

Your model is slow. Your orchestration layer does not have to be.

Your AI stack gets an orchestration layer with built-in deterministic validation that costs less than 2 ms — even under load. Typical LLM completions land around 820 ms; ValGuard's measured validation layer adds 5.01 ms of HTTP overhead (zero-latency mocked upstream) and a full multi-agent playbook path adds 0.35 ms p50 — deterministic rules, not another model round-trip.

Full benchmark report →Load & saturation charts →

Typical LLM completion820 ms

ValGuard guarded proxy path5.01 ms

Same scale as above — 0.61% of one model call. That thin sliver is the point.

Up to ~218×

lower validation layer overhead vs model latency

Full playbook p50 0.35 ms — ~2,349× lower than model

Same scale, one glance. ValGuard guarded proxy path measured on a zero-latency mocked upstream (chat upstream guarded; median of 5 runs, headline uses best run).

≤ 26 µs

Deterministic validator

0 allocations

349.1 µs

p50

p95 3.72 ms · p99 6.96 ms

Full multi-agent playbook

routing + every check, mocked upstream

820 ms

Your model call

Even with full orchestration, ValGuard adds sub-millisecond overhead — a tiny fraction of the model you already call.

What changes after ValGuard?

Before ValGuard

After ValGuard

Before ValGuard

Complex multi-agent routing requires bloated Python code and state management

After ValGuard

Visually map agent graphs. ValGuard handles memory, state, and conditional routing automatically at the edge

Before ValGuard

One agent's hallucination cascades and breaks the entire multi-agent workflow

After ValGuard

In-the-loop validation traps errors instantly. Failed steps trigger microsecond re-asks before impacting downstream agents

Before ValGuard

Every deployment feels like a gamble

After ValGuard

Deterministic validation runs automatically on every step of the flow

Before ValGuard

Bad data silently corrupts the next agent in the chain

After ValGuard

Issues are caught in microseconds, before they ever reach the downstream step

Before ValGuard

Every request pays premium-model prices, even the ones a cheap model could have handled

After ValGuard

Validation-gated fallback escalates to a stronger model only when the cheap one fails a check — budgets and token caps hold the ceiling

Before ValGuard

You find out about a bad run from a support ticket, weeks after it shipped

After ValGuard

Every request, validation result, and blocked step lands in an audited log — dashboards and alerts surface drift before it becomes an incident

Before ValGuard

Customers discover your AI mistakes before you do

After ValGuard

Your team catches issues first — and fixes them without redeploying

For risk, compliance & business owners

You Don't Need to Write Code to Sleep at Night

Your engineering team already recommends ValGuard. Here's what you — as the person accountable for risk, compliance, or the business outcome — get without touching a line of code.

See it before your customer does

The dashboard shows every rule that blocked or flagged an agent's response, in real time. No need to ask an engineer for a log.

Change a rule without a ticket

Toggle a template, adjust a threshold, approve a change — one click, with automatic version history (who, when, what).

Walk into an audit with proof

Export the full audit trail to PDF/CSV, ready for internal or external review, with nothing to prepare by hand.

See a sample audit report

Send this to your team

No account required for the sample report. Forward the handoff email to your risk, compliance, or executive sponsor in one click.

How ValGuard fits your stack

The ValGuard AI Management Layer

Orchestration, validation, cost control, and observability run at the edge between your supervisor, agents, and tools. ValGuard enforces routing, policy, spend limits, and audit trails in one layer. Decisions land in microseconds with no extra services or glue code.

Orchestration

Routes supervisor flows through agents, conditional branches, and tool calls with deterministic timing at the edge. Production-ready playbooks — linear, branch, and fan-out paths you can provision in one click.

51 playbooks

Validation

Applies built-in schema checks, policy packs, and business rules on every step. Failed output triggers microsecond re-asks before the next agent sees it.

160 validators 116 templates

Cost Control

Enforces per-agent budgets, token caps, and cheap-first model fallback on every request so runaway LLM spend never reaches production. Set org-wide validation quotas and token burn limits, with optional overage alerts before invoices spike.

Observability

Captures a full audit trail, structured validation logs, and drift alerts across every agent, playbook step, and tool call — no separate APM stack required. Drill from dashboard trends to per-request verdicts, block rates, and token burn without exporting to third-party tools.

Stop silent failures before they ship

Real failure modes we catch

Multi-agent systems do not fail with HTTP 500 — they fail at the seams between agents, with plausible-looking output that misroutes, corrupts state, or loops forever. These are the patterns we block before they cascade.

Routing failures

An LLM-based router guesses the next agent — and sends a finance request to the logistics agent. ValGuard routes on deterministic conditions over real fields, never a guess.

route → "logistics_agent"
// finance request misrouted

json_field_equalson_validation_pass

State corruption

One agent hands the next malformed or half-formed data, and the pipeline collapses two steps downstream. Every handoff is schema-checked before it moves on.

{ "amount": "12.0.0", "currency": null }

valid_formatrequired_fields

Integrate in minutes, not sprints

One call. A validator — or a whole orchestration — in front of your AI.

Keep your existing model calls. Orchestrate multiple agents into a guarded playbook — or drop in a single deterministic validator — over the same OpenAI-compatible endpoint. Just a new base URL and one header, and the orchestration layer adds microseconds, not seconds.

Orchestration

Validators

Support · playbook

Support Automation

Understand the ticket, draft a safe customer reply, and route to a human when the draft fails safety checks.

Otherwise (if nothing above matches)

When everything looks good

When the answer isn't safe enough

Otherwise (if nothing above matches)

Understand the ticket

valid format

Draft safe reply

min length

Hand off to agent

valid format

Send to customer

Handoff complete

Each step runs multiple deterministic checks (schema, business rules, policy, format) — all executed in parallel.

Steps in this playbook

1
Understand the ticket
valid format · 7 output checks · input filtered
2
Draft safe reply
min length · 7 output checks
3
Hand off to agent
valid format · 8 output checks

Language

client.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["VG_API_KEY"],
    base_url="https://api.valguard.ai/v1",
    default_headers={"X-VG-Flow": "support-automation"},
)

response = client.chat.completions.create(
    model="openai/gpt-4o-mini",
    messages=[{"role": "user", "content": "I want a refund for order #8842. You promised me a guaranteed refund in your email."}],
)

// 200 OK — reply delivered
// flow completed and delivered

Orchestration overhead

3 steps · p50 1.82 ms

Routing + every per-step check · ~450× faster than the model · measured.

Build a playbook Getting Started Browse all 116 templates

Teams shipping guarded AI

Guardrails teams keep on after launch

Regulated and high-volume workflows — invoice extraction, clinical intake, contract redlining, resume screening, insurance claims, logistics dispatch, security alerts, ad variants — orchestrated and validated deterministically on every step, with cost and audit trails intact, not reviewed after the fact.

We auto-approved invoices until a model invented a vendor tax ID. The Invoice Processing playbook blocks malformed extraction before ERP ingest — shadow mode surfaced the gap in a day without touching live traffic.

Caught~38 bad extractions flagged in week 1 (shadow)
Savings~$11k/quarter avoided ERP reversals

Sarah K. · FinTech

Playbook: Invoice Processing →

Our portal bot drafted reassuring copy that understated contraindications. Medical Script Guard routes unsafe patient messages to nurse review instead of inboxes.

Caught~12 unsafe drafts routed / month
Time~7 nurse-review hours saved vs full rewrites

Marcus S. · Healthcare

Playbook: Medical Script Guard →

Refund promises were our nightmare — upbeat tone, wrong policy. Refund Request Handling catches unauthorized commitments on every support draft while we keep the fast multi-agent handoff.

Caught~180 unauthorized refund phrases blocked / month
Savings~$3.5k/month fewer policy reversals

Elena R. · E-commerce

Playbook: Refund Request Handling →

Validation and orchestration on every plan

Simple, transparent pricing

Every plan includes the full orchestration layer — all validators, every template, and multi-agent playbooks. Built-in deterministic validation guards every agent and every step, with no metered runs and no feature gates. Upgrade only when you need longer log retention and deeper analytics. Monthly requests?are counted per flow run.