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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

    Health score, rank, 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 DemoExplore 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 0.38 ms of HTTP overhead (zero-latency mocked upstream) and a full multi-agent playbook path adds 2.14 ms p50 — deterministic rules, not another model round-trip.

Full benchmark report →Load & saturation charts →

Typical LLM completion820 ms
ValGuard guarded validation path0.38 ms

Up to ~2,426×

lower validation layer overhead vs model latency

Full playbook p50 2.14 ms — ~383× lower than model

Same scale, one glance. ValGuard guarded validation 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

2.14 ms

p50

p95 6.17 ms · p99 10.57 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

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 at the handoff. Deterministic rules run in microseconds; configured re-asks call the model again before downstream agents proceed

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

Rule checks run in microseconds; blocks and repairs stop bad payloads before the next step sees them

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. Deterministic rule checks run in microseconds on the engine path; routing, playbooks, and configured re-asks add orchestration and model time on top.

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.

63 playbooks

Validation

Applies built-in schema checks, policy packs, and business rules on every step. Engine checks run in microseconds; block, repair, or re-ask actions are configured per rule (re-ask adds another model attempt).

160 validators144 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 a transparent agent health score (0–100) that ranks every agent, powers league tables, and fires score-drop alerts — plus drift signals across playbook steps and tool calls. 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. Engine rule packs add microseconds on the hot path; model calls and configured re-asks still dominate latency. See integrations for LangChain, gateways, voice, and n8n.

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)
Input filters active

Understand the ticket

valid format

7 output checks7
No input filters

Draft safe reply

min length

7 output checks7
No input filters

Hand off to agent

valid format

8 output checks8
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. 1

    Understand the ticket

    valid format · 7 output checks · input filtered

  2. 2

    Draft safe reply

    min length · 7 output checks

  3. 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 1.95 ms

Routing + every per-step check · ~421× faster than the model · measured.

Build a playbookGetting StartedBrowse all 144 templates

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. Free includes 10,000 requests per month (hard cap), shadow mode, and full provider access — upgrade when you need higher limits, log retention, and analytics. Monthly requests?bill one request per single-agent flow; in playbooks, each live agent step with an upstream call counts as one request.

Estimate cost with the calculator →Full pricing, discounts & billing →Playbooks →Validation templates →

Ship your first playbook today

Ready to orchestrate AI agents you can trust?

Pick a playbook, provision every agent and its validators in one click, and run your first guarded multi-agent flow in minutes.