Services
Implementation scope and rollout planning
Clear next-step recommendation
Build a central command system to coordinate specialized AI agents, ensuring seamless collaboration and reliable business outcomes.
Deploying individual AI agents creates islands of automation. Without orchestration, you face:
We engineer central control platforms using frameworks like
LangGraphandAutoGento sequence tasks, manage state, and synthesize final results from your agent workforce.
Deliverables include:
Move from fragmented automation to a cohesive, intelligent system. Explore our broader approach to Multiagent Systems (MAS) Architecture or learn how we secure these networks with Multiagent System Security Architecture.
Our multiagent orchestration platform development delivers more than just technical coordination. It translates directly into measurable business advantages, accelerating time-to-market, reducing operational risk, and unlocking new levels of AI-driven efficiency.
Deploy complex, collaborative AI workflows in weeks, not months. Our platform engineering with LangGraph and AutoGen provides pre-built, battle-tested orchestration patterns, eliminating the need to build foundational coordination logic from scratch.
Key Deliverables:
Achieve consistent SLAs for multi-step AI processes under variable load. Our architecture ensures reliable agent execution, intelligent queuing, and resource-aware scheduling, preventing cascading failures and meeting strict throughput requirements.
Key Deliverables:
Centralize the management, monitoring, and security of your entire agentic workforce. A single pane of glass for logging, tracing, and cost attribution replaces the overhead of managing disparate scripts and microservices, leading to significant OpEx savings.
Key Deliverables:
Ensure the final output of collaborative agent chains is coherent, accurate, and actionable. Our orchestration logic manages context aggregation, validates intermediate results, and applies business rules to synthesize a single, trustworthy outcome from distributed agent work.
Key Deliverables:
Build on a platform designed for evolution. Easily integrate new agent types, swap underlying LLMs, or adopt emerging frameworks without re-architecting your core coordination logic, protecting your investment as the multiagent ecosystem rapidly advances.
Key Deliverables:
A transparent breakdown of the phased development process for a custom multiagent orchestration platform, from initial architecture to full-scale deployment and ongoing support.
| Phase & Key Deliverables | Starter (Proof-of-Concept) | Professional (Production-Ready) | Enterprise (Scaled Deployment) | |
|---|---|---|---|---|
Architecture & Design (Weeks 1-2) | Core agent role definitions & basic LangGraph workflow diagram | Full system architecture, security model, and integration spec | Enterprise-scale topology with failover, DR, and compliance mapping | |
Core Orchestrator Development (Weeks 3-6) | Basic sequential agent coordination with manual error handling | Robust LangGraph/AutoGen state machine with automated handoffs & retry logic | Advanced orchestration with dynamic routing, load balancing, and agent negotiation protocols | |
Agent Integration & Tooling (Weeks 7-10) | Integration of 2-3 core agents (e.g., LLM, RAG) | Integration of 5-8 specialized agents with custom tools & APIs | Integration of 10+ agents, legacy system adapters, and real-time data connectors | |
Security & Observability Layer | Basic API authentication | Agent-level auth, audit logging, and basic monitoring dashboard | Full MITRE ATLAS-aligned security, real-time collaboration analytics, and anomaly detection | |
Testing & Validation | Unit tests for core orchestration logic | End-to-end workflow simulation & adversarial testing suite | Load testing at scale, red teaming, and compliance validation (e.g., for AI Act) | |
Deployment & Go-Live Support | Deployment to a single cloud environment with documentation | CI/CD pipeline, cloud-agnostic deployment, and 2 weeks of launch support | Multi-region/ hybrid-cloud deployment, full knowledge transfer, and dedicated SRE handoff | |
Ongoing Maintenance & Scaling | Ad-hoc support | Optional SLA with priority support and quarterly reviews | Managed service option with 99.9% uptime SLA, performance tuning, and roadmap planning | |
Typical Timeline | 8-10 weeks | 12-16 weeks | 16-24+ weeks (scalable phases) | Typical Investment Range$50K - $80K$120K - $250K$300K+ (custom quote) |
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get specific answers on timelines, costs, and technical capabilities for building a central control system to coordinate specialized AI agents.
A standard deployment for a foundational orchestration platform using frameworks like LangGraph or AutoGen takes 2-4 weeks. This includes core agent coordination, basic task handoff logic, and a single integration point. Complex deployments with 10+ specialized agents, custom communication protocols, and multiple legacy system integrations typically require 6-10 weeks. We provide a phased roadmap, often delivering a minimum viable orchestrator in the first 2 weeks to validate the approach.
5+ years building production-grade systems
We look at the workflow, the data, and the tools involved. Then we tell you what is worth building first.
The first call is a practical review of your use case and the right next step.
