// SERVICE
Multi-Agent System Development
Chatbots answer questions. Agentic systems do work. We build production multi-agent architectures — specialized agents that collaborate through defined workflows, with the guardrails and observability required to trust them with real tasks.
The difference between a chatbot and a multi-agent system
A chatbot is a single LLM call wrapped in a conversation loop. A multi-agent system is an orchestra of LLM-powered workers, each with a specialized role, a bounded scope, and defined handoff rules — coordinated by a controller that knows when to parallelize, when to escalate, and when to stop.
The practical distinction: a chatbot answers "what's the status of ticket 4521?" A multi-agent system watches your inbox, triages incoming requests, drafts responses, gets them reviewed, files them back, and reports anomalies to a human — continuously, without being asked.
Frameworks we ship on
AutoGen
Microsoft Research's conversational multi-agent framework. Our default for workflows that involve back-and-forth reasoning — code generation, research synthesis, financial analysis. Strong at role-based collaboration (planner → coder → reviewer → tester) and at letting agents call functions and tools. Integrates cleanly with local LLMs via an OpenAI-compatible endpoint.
CrewAI
Role-first orchestration for business workflows. Cleaner abstraction when the problem looks like a team-of-specialists (strategist, writer, editor, publisher) executing a sequential or hierarchical process. Lighter footprint than AutoGen, faster to ship for well-bounded tasks.
Custom agentic scaffolds
Not every system should use an off-the-shelf agent framework. When the workflow is narrow and the stakes are high, a purpose-built orchestrator with explicit state machines outperforms a general-purpose agent loop. We build those too. 60+ open source repos of prior art — TeamForgeAI, ai-persona-lab, Ollama-Workbench — inform every build.
Where multi-agent systems pay off
- Research and analysis workflows. One agent plans the investigation, others execute web searches or database queries, a synthesizer compiles, a critic reviews. Replaces a workflow that currently eats three analysts a week.
- Sales and marketing operations. Lead enrichment, proposal drafting, sequence personalization, pipeline hygiene — handed to a crew of agents with a human approval gate before anything sends.
- Engineering and code operations. Spec-to-PR pipelines where a planner drafts an approach, an implementer writes code, a reviewer catches regressions, a tester validates. Shipping, not vibes.
- Customer support escalation. First-line agents handle routine tickets; escalation agents route complex ones to humans with full context summaries. See our customer support AI service.
Where they don't
If the task is a single call ("summarize this document," "classify this email"), a multi-agent system is overkill and you'll just add latency and cost. We will tell you that. Not every problem is an agent problem.
What production-grade actually requires
Guardrails
Output validation, refusal handling, tool-use allowlists, maximum iteration limits, budget caps. Every agent knows what it's allowed to do and what it's not. Production systems don't have agents writing files outside a sandbox or calling APIs they weren't authorized for.
Observability
Every agent call logged with prompt, completion, tool invocations, token counts, latency. Replayable traces for debugging. Metrics dashboard for aggregate behavior. When something goes wrong at 3 AM, you can see exactly which agent did what.
Human-in-the-loop checkpoints
Anything with real-world consequences (sending external email, publishing content, executing trades, modifying production data) passes through a human approval queue. Approval UI is part of the build, not an afterthought.
Evaluation harness
Test set of realistic scenarios with ground-truth outcomes. Regression testing on every prompt change, model swap, or workflow edit. Continuous measurement, not vibes.
Pricing
A scoped POC ($25K–$60K, 4–6 weeks) ships one multi-agent workflow end-to-end on your infrastructure. A production build ($75K+, 8–16 weeks) hardens it for real users and real data. Start with the free AI Readiness Assessment to see whether a multi-agent approach fits your problem.
Frequently asked questions
What is a multi-agent system?
A multi-agent system uses multiple specialized AI agents that collaborate to complete complex tasks autonomously. Unlike a chatbot that responds to one query at a time, a multi-agent system assigns roles — researcher, analyst, writer, reviewer — and agents work together through defined workflows with orchestration, guardrails, and human checkpoints.
AutoGen vs CrewAI — which do you use?
Both, depending on the problem. AutoGen is stronger for conversational, iterative reasoning workflows (code generation, research synthesis). CrewAI is cleaner for role-based business workflows (strategist → writer → editor → publisher). We pick after discovery — we do not force a framework onto a problem that fits the other one better.
Can multi-agent systems run on local LLMs?
Yes. Both AutoGen and CrewAI support any OpenAI-compatible endpoint, which means they work with locally-deployed Ollama, vLLM, or llama.cpp servers. Most of our agentic builds run entirely on client-owned infrastructure with zero cloud dependency. See local AI deployment.
How do you prevent agents from going off the rails?
Four layers: (1) tool-use allowlists so agents can only call authorized functions, (2) maximum iteration and budget limits so runaway loops self-terminate, (3) output validation against schemas before any state-changing action, and (4) human approval gates for anything with external consequences. Every action is logged and replayable.
What's the difference between an agent and a workflow automation tool like Zapier?
Zapier-class tools execute predefined steps. An agent chooses which steps to execute based on the input. An agent can read an email, decide whether it needs a response, look up relevant context in three different systems, draft a reply, and ask a human to approve — without that path being hardcoded. The flexibility is the point; the guardrails are what makes it production-safe.
Ready to start?
Book a free 30-minute AI Readiness Assessment. No pitch deck. No retainer ask. Just a working session to map your stack and surface the two or three highest-ROI AI interventions for your situation.