Multi-Agent Systems

Multi-Agent System Development

Some business problems are too complex for a single AI agent. Multi-agent systems solve this by orchestrating teams of specialized agents that collaborate — one researches, one drafts, one reviews, one acts — completing workflows that would take a human team hours, in minutes. SaTekk designs and builds production multi-agent systems using LangGraph, CrewAI, and custom orchestration patterns, tailored to your specific workflows.

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Multi-agent architectures we build

01

Supervisor-Worker Architecture

A coordinator agent breaks tasks into subtasks and assigns them to specialist agents — enabling parallel execution and autonomous decision-making.

02

Sequential Pipeline Agents

Chains of agents where each one processes and enriches the output of the previous — ideal for document review, research summarization, and report generation.

03

Research & Synthesis Agents

Multi-agent systems that autonomously browse the web, query APIs, synthesize findings, and produce structured reports — replacing entire research workflows.

04

Debate & Review Patterns

Adversarial agent patterns where one agent drafts, another critiques, and a third arbitrates — producing higher-quality outputs than any single agent.

05

Shared Memory & State Management

Persistent shared memory stores that let agents within a system share context, avoid duplication, and maintain consistent state across long-running tasks.

06

Human-in-the-Loop Controls

Configurable approval gates that pause agent execution at defined checkpoints, letting your team review and guide autonomous systems in high-stakes workflows.

Free
30-min strategy call
100%
source-code ownership
Fixed
timeline & pricing
30d
post-launch support

Frequently asked questions

When do I need a multi-agent system vs. a single agent?+
Use a single agent for focused, well-defined tasks. Use multi-agent when: the task is too complex for one agent's context window, different subtasks need specialized models, you want parallel execution to save time, or you need checks and balances (one agent reviewing another). Multi-agent adds overhead, so we only recommend it when it genuinely improves outcomes.
Which frameworks do you use for multi-agent development?+
LangGraph is our default for stateful, graph-based agent orchestration — it handles complex branching logic and human-in-the-loop patterns well. CrewAI is excellent for role-based crew patterns. For simpler orchestration we sometimes use raw LangChain or fully custom Python. We recommend based on your complexity and team's ability to maintain the system.
How do you ensure multi-agent systems are reliable in production?+
Reliability comes from explicit state management, deterministic routing logic, comprehensive logging of every agent action, retry/fallback handling, and human-in-the-loop gates for critical decisions. We also build monitoring dashboards so you can observe system behavior and intervene when needed.
How long does a multi-agent system take to build?+
A well-scoped multi-agent system with 3–5 agents typically takes 4–8 weeks. Complex systems with many agents, shared memory, and sophisticated orchestration take 8–16 weeks. We always scope multi-agent builds carefully — it's easy to over-engineer, and we push back when a simpler solution will work.
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Ready for AI that works like a team?

Book a free call. We'll map your workflow, identify where multi-agent orchestration adds genuine value, and design the right architecture.

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