AI Services / Agent Ecosystem

Deploy Networks of AI Agents That Work Together

Specialized AI agents that collaborate, share memory, and orchestrate complex multi-step tasks, with enterprise-grade governance and oversight.

10×
Throughput
24/7
Operation
<200ms
Latency
99.9%
Uptime
MULTI-AGENT ORCHESTRATIONACTIVE
Orchestrator
MASTER AGENT
01
Research
RAG · Web · Retrieval
LLM
02
Writer
Content · Copy · Docs
LLM
03
Code
Gen · Review · Exec
LLM
04
QA
Eval · Score · Validate
TOOL
05
Deploy
CI/CD · Infra · Ship
API
MEMORY
Vector store · Context · State
10×
Throughput
24/7
Operation
<200ms
Latency
99.9%
Uptime

Capabilities

What Your Agent Network Can Do

Multi-Agent Coordination

Master orchestrators route tasks to specialized sub-agents based on capability, load, and context. Parallel execution for complex workflows.

Shared Memory Architecture

Agents share a common memory layer. One agent's findings instantly available to all others in the same workflow.

Tool Use & Action Execution

Agents call APIs, query databases, write code, send emails, and take actions in external systems, with full audit trails.

Reasoning Chains

Agents decompose complex goals into sub-tasks, plan execution sequences, and reason through uncertainty before acting.

Failure Recovery

Agents detect task failures, retry with different strategies, route to backup agents, or escalate to humans, automatically.

Cost & Token Optimization

Smart model routing: lightweight tasks use fast/cheap models; complex reasoning uses frontier models. Automatic cost management.

Process

How We Build Your Agent Ecosystem

01

Define Agent Roles

We map your use case to an agent architecture, which agents are needed, what tools each can use, and how they coordinate.

Use case analysisAgent role definitionTool permission designMemory architecture design
02

Build Agent Network

We build and configure each agent with its instructions, tools, memory access, and escalation rules.

Agent instruction engineeringTool integrationMemory configurationEscalation rule setup
03

Connect Data Sources

Agents are connected to your knowledge bases, APIs, databases, and external services they need to operate.

RAG pipeline setupAPI integrationsDatabase connectorsExternal service auth
04

Test & Red-Team

We stress-test the agent network, adversarial inputs, edge cases, failure scenarios, before any production traffic.

Adversarial testingEdge case simulationFailure mode analysisGovernance policy testing
05

Deploy & Monitor

Production deployment with real-time dashboards showing agent activity, costs, decision chains, and performance.

Staged rolloutReal-time monitoring dashboardCost trackingDecision audit logs

Before / After

Manual Operations vs. Agent Network

Metric
Without Agents
With Solnix Agent Network
Task throughput
Limited by headcount
10× parallel agent execution
Operating hours
Business hours only
24/7/365
Task consistency
Varies by employee
100% consistent execution
Knowledge access
Siloed by person/team
Shared across all agents
Failure recovery
Manual, slow
Automatic retry + escalation
Audit trail
Incomplete
Full decision chain logged

Stack

Technology Stack

Orchestration
LangGraphCrewAIAutoGenCustom DSPy
Memory
Pinecone, Vector storeRedisZepmem0
Models
GPT-4oClaude 3.5Gemini 1.5 ProLlama 3.3
Tools
BrowserbaseE2B Code InterpreterComposioCustom APIs
Observability
LangSmithArizeWeights & BiasesOpenTelemetry

FAQ

Common Questions

Get Started

Deploy your first AI agent network in 30 days

From architecture design to production deployment, we handle the full build.

Talk to usRequest a demo