Now in Beta

Built on kagent (CNCF)

Run AI agents as isolated,
reliable services.

Consonant is a service mesh that prevents cascading failures, noisy neighbors, and scaling bottlenecks in multi-agent systems.

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🚫 BEFORE CONSONANT

Monolithic Container

System Failure

Ag A

Ag B

Ag C

Ag D

One Container • Shared Resources

❌One agent crashes → All agents die (100% Outage)
❌Resources wasted (provisioned for peak of ALL agents)
❌Update one agent → Redeploy everything

✅ AFTER CONSONANT

Independent Agents

Ag A

2 CPU

Restarting

Ag B💀

0.2 CPU

Ag C

Scaled x5

Ag D

0.1 CPU

✅Failures isolated (One crashes, others stay up)
✅Resources right-sized (Save 40% on compute)
✅Independent deploys (Update Agent A in seconds)

WHAT BREAKS WHEN YOU SHIP
MULTI-AGENT SYSTEMS

Infrastructure failure is the #1 killer of agent reliability.

0ms

Minor Latency Spike

A vector database or external tool slows down by 200ms.

Unbounded Retry Loop

Agent A enters a loop, recursively calling tools while consuming 90% CPU.

12s

Shared process OOMs

Agent A OOMs → ALL AGENTS DIE

Retry loops AMPLIFY FAILURES

Tool dependency STALLS ENTIRE SYSTEM

💥 PRODUCTION DEPLOYMENT FROZEN

Engineering Impact Analysis

Blast Radius100% Shared Fate

Resource CouplingWaste Math

Cost Explosion

Recursive Tool Burn

Iteration SpeedLocked to Monolith

ObservabilityImpossible (No isolation)

"Your agents are one memory leak away from a total system outage."

Stop deploying agents as scripts.
Start deploying them as isolated, managed services.

The Production Disasters

Multi-agent systems inevitably hit these walls when running in a single process.

Agent blast radius

One agent crashes → All 5 agents restart.

Common in LangGraph / CrewAI / custom agent stacks

📊 The Damage

18 days downtime/year
100% system outage
$2.75M revenue lost

Consonant Solution

20% degradation
0 day downtime
$2.75M saved

Shared memory death

One greedy agent eats 100% RAM, starving the router process.

Common in LangGraph / CrewAI / custom agent stacks

📊 The Damage

40% compute wasted
$20k/year overspend
Idle resources billing

Consonant Solution

Right-sized pods
Auto-scaling 0-100
Pay for usage only

Coupled deployments

Typos in one agent break the entire monolith.

Common in LangGraph / CrewAI / custom agent stacks

📊 The Damage

Fear of Friday deploys
208 hours frozen/yr
Rollbacks take hours

Consonant Solution

Isolated rollouts
Canary deployments
2 min recovery

Unbounded retries

Retry loops in one agent amplify failures across the cluster.

Common in LangGraph / CrewAI / custom agent stacks

📊 The Damage

9x resource bloat
Saturated DB connections
Capped throughput

Consonant Solution

Policy-based retries
Circuit breaking
Uncapped growth

Dependency Prison

Python version conflicts & locked into one framework forever.

Common in LangGraph / CrewAI / custom agent stacks

📊 The Damage

Can't use best tools
Venv nightmares
Rewrite to switch tools

Consonant Solution

Any language/lib
Mix LangGraph/CrewAI
Container isolation

Resource Monopoly

Scaling your email agent? You must scale your light heavy researcher too.

Common in LangGraph / CrewAI / custom agent stacks

📊 The Damage

Unpredictable latency
Random timeouts
Hard to debug

Consonant Solution

Strict CPU limits
Guaranteed QoS
Predictable perf

If you have 3+ agents in production, you've hit at least five of these.
Consonant solves all.

The Complete Architecture

Most teams run all agents in one process, container, or job. Consonant turns each agent into an independent service with clear boundaries.

Any Framework Independent Scaling Failure Isolation

1. USER

"Database slow, fix it"

2. CONSONANT CONTROL PLANE

Intelligence

Routes to right agent

Policy Engine

Checks permissions

Observability

Traces everything

3. RELAYER

4. AGENT RUNTIME (ANY FRAMEWORK) - Independent scaling & failure domains

DB Agent

K8s Agent

Cost Agent

Sec Agent

Each runs in a separate container • Independent scaling • Fault isolated

Intelligence

LLM decides which agent to use based on the user request and agent capabilities description.

Policy Engine

OPA enforces governance (SOC2-ready). No action is taken without a policy check pass.

Observability

Complete audit trail. Every input, output, and tool call is traced and recorded.

DEVELOPER STORY

When your agents meet
real production traffic

What happens when your retrieval agent slows down but your planner doesn’t?
In a monolith, the slow agent drags the entire cluster to zero.

Isolated

Failure Domains

Granular

Auto-scaling

0ms

Traffic Surge

Incoming RAG requests spike by 500%.

Agent Stalling

retrieval-agent slows to 8s latency.

Independent Scaling

Consonant scales retrieval x10. planner stays idle.

12s

System Stabilized

Planner performance remains 100% unaffected.

The Impact of Agent Isolation

Whether you're writing the code or managing the cluster, Consonant changes the math of production AI.

Engineer ROI

Debug independently

Trace one agent's tool calls without 100k lines of unrelated logs.

Restart one, not all

Fix a bug in the "Auditor" agent without killing "The Planner".

Scale only what's slow

Add 50 nodes to Retrieval while keeping your Controller tiny.

Faster Iteration

Script-to-Prod in minutes. No deployment fear.

Platform ROI

Infrastructure Savings

Save up to 40% on compute by right-sizing agent pods and avoiding monolithic over-provisioning.

Compliance & Governance

Out-of-band OPA enforcement ensures all agent actions are logged and stay within SOC2/HIPAA guardrails.

40%

Cost Reduction

Before:$2,920/mo (monolithic)

After:$1,250/mo (right-sized)

$20k/yr saved

75%

Less Risk

Before:208 hrs/yr fragile

After:35 hrs/yr risk

Ship 3x faster

90%

Scaling Efficiency

Before:Scale ALL agents

After:Scale per agent

$150k/yr peak savings

99.9%

System Uptime

Before:18 days downtime

After:0 day downtime

$2.75M revenue protected

Who This Is For

AI Engineers

Building multi-agent systems with LangGraph, CrewAI, or custom frameworks.
Hitting scaling, latency, or reliability bottlenecks.
Tired of managing shared failure domains.

"Build reliable agents, not fragile scripts."

Startup ML Teams

You’ve outgrown notebooks and local demos.
Fragmented codebases are slowing your iteration speed.
Need a production-grade infrastructure layer today.

"Scale beyond the POC phase in minutes."

Platform / Infra Teams

You need governance, safety, and isolation at scale.
Require SOC2-ready audit trails for every agent action.
Need to manage multi-tenant agent fleets.

"Provide a safe, isolated runtime for your AI teams."

Compare Approaches

Feature	Single-process agents	Framework-only orchestration	Agent services (Consonant)
Blast Radius	100% (All crash)	Manual setup	<20% (Isolated)
Deploy Time	Hours	30 min	1 min
Governance	None	Custom build	Built-in (OPA)
Observability	Logs only	Custom	Complete Traces
Time to Prod	Months	Months	One day

DEVELOPER FIRST

Start Building in Minutes

Consonant is a single binary that works with your existing Kubernetes cluster.

Works with existing agents — no rewrites.

1. Install Consonant

Install the CLI and control plane in one command.

# Install CLI
curl -sL https://get.consonant.dev | bash

# Initialize Control Plane (in your K8s cluster)
consonant install control-plane --namespace consonant-system

2. Wrap Your Agent

Wrap an existing agent in minutes. Create a manifest with resource limits and capabilities.

# agent.yaml
apiVersion: consonant.dev/v1
kind: Agent
metadata:
  name: researcher
spec:
  image: agents/researcher:latest
  resources:
    cpu: "2"
    memory: "4Gi"
  scaling:
    minReplicas: 1
    maxReplicas: 10

3. Run & Orchestrate

Deploy and let the control plane manage the lifecycle.

# Deploy the agent
consonant apply -f agent.yaml

# Run a task
consonant run "Research competitors for 'AI Orchestration'"

terminal

Step 1

# Install CLI
curl -sL https://get.consonant.dev | bash

# Initialize Control Plane (in your K8s cluster)
consonant install control-plane --namespace consonant-system

Step 2

# agent.yaml
apiVersion: consonant.dev/v1
kind: Agent
metadata:
  name: researcher
spec:
  image: agents/researcher:latest
  resources:
    cpu: "2"
    memory: "4Gi"
  scaling:
    minReplicas: 1
    maxReplicas: 10

Step 3

# Deploy the agent
consonant apply -f agent.yaml

# Run a task
consonant run "Research competitors for 'AI Orchestration'"

Get Started

Try Locally

Run the full platform on your laptop in 5 minutes.

# Install

helm install consonant ./chart

# Deploy

kubectl apply -f my-agents/

# Run

consonant run --goal "test"

Quickstart Guide

Deploy to Production

Ready for real traffic? Use our production-grade features.

Multi-cluster support
Policy enforcement (OPA)
Complete observability

Architecture Docs

See the ROI

Why enterprises switch to independent agent runtimes.

40% infrastructure savings
Compliance-ready (SOC2/HIPAA)
Zero vendor lock-in

Schedule Demo

Built on

solo.io kagent

GitHub Documentation Contact Sales

Run AI agents as isolated, reliable services.

🚫 BEFORE CONSONANT

✅ AFTER CONSONANT

WHAT BREAKS WHEN YOU SHIP MULTI-AGENT SYSTEMS

Minor Latency Spike

Unbounded Retry Loop

Shared process OOMs

Engineering Impact Analysis

The Production Disasters

Agent blast radius

Shared memory death

Coupled deployments

Unbounded retries

Dependency Prison

Resource Monopoly

The Complete Architecture

Intelligence

Policy Engine

Observability

When your agents meet real production traffic

The Impact of Agent Isolation

Who This Is For

AI Engineers

Startup ML Teams

Platform / Infra Teams

Compare Approaches

Start Building in Minutes

1. Install Consonant

2. Wrap Your Agent

3. Run & Orchestrate

Get Started

Try Locally

Deploy to Production

See the ROI

Run AI agents as isolated,
reliable services.

WHAT BREAKS WHEN YOU SHIP
MULTI-AGENT SYSTEMS

When your agents meet
real production traffic