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 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 0.52 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 path0.52 ms

Up to ~1,872×

Mocked-upstream overhead compared with typical LLM latency, not a live model call.

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).

≤ 45 µs

Deterministic validator

1 alloc on the heaviest measured pack

2.14 ms

p50

p50 and p95 · 2.14 ms / 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

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.

71 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 validators 144 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

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.