Overview
Complex business workflows cannot be solved by a single AI agent. Multi-agent systems decompose hard problems into specialised sub-tasks, assigning each to an agent with the right tools, context, and capabilities — then coordinating their outputs into a coherent result. We design and build production multi-agent systems where agents plan, delegate, collaborate, and verify each other’s work. From financial analysis workflows that span data retrieval, modelling, and narrative generation, to drug discovery pipelines coordinating literature review, hypothesis generation, and experimental design — we architect agent networks that work reliably at enterprise scale.
How It Works with a21
Workflow Analysis & Agent Design
Decompose your target workflow into tasks, subtasks, and decision points. Design the agent architecture — roles, responsibilities, communication patterns, and escalation logic.
Agent Development & Tool Integration
Build individual agents with their specific capabilities, tool access, and prompt architecture. Implement the orchestration layer — routing, state management, and inter-agent communication.
Testing, Hardening & Deployment
Test the agent network across workflow scenarios including edge cases and failure modes. Implement monitoring, logging, and human-in-the-loop checkpoints. Deploy to production.
What We Offer
Workflow Decomposition
Analyse complex business workflows and decompose them into agent-appropriate tasks — identifying where specialisation, parallelism, and coordination add value.
Orchestration Architecture
Design the orchestration layer — hierarchical (orchestrator + workers), peer-to-peer, or market-based — matched to your workflow characteristics and reliability requirements.
Specialist Agent Development
Build domain-specialist agents with specific tool access, knowledge sources, and capability profiles — from data analyst agents to compliance reviewer agents.
Inter-Agent Communication
Implement structured communication protocols between agents — ensuring context, results, and state are passed reliably across the agent network.
Human-in-the-Loop Checkpoints
Design human oversight and approval gates at critical decision points — ensuring humans remain in control of high-stakes outcomes.
Agent Monitoring & Observability
Implement logging, tracing, and dashboards for agent network behaviour — making it possible to debug, audit, and improve the system in production.
Why Choose a21
Production-Grade Reliability
We build agent networks that work reliably in production — not just in demos. Our architectures include error handling, retry logic, and graceful degradation.
MCP-Native Integration
We build agent systems using Model Context Protocol — giving agents standardised, secure access to your enterprise systems, databases, and APIs.
Audit Trails by Default
Every agent action, tool call, and decision is logged. Our systems produce the audit trails that regulated industries require.
Domain Expertise
We design agent workflows grounded in how your industry actually operates — not generic examples that fail when they meet real business complexity.
Success Stories
Problem
An asset manager wanted to automate equity research — a process spanning data gathering, financial analysis, sentiment analysis, peer comparison, and report drafting.
Solution
Built a five-agent network: a data retrieval agent, financial analysis agent, sentiment analysis agent, peer comparison agent, and report drafting agent — coordinated by an orchestrator.
Problem
A pharma company’s regulatory affairs team spent weeks manually aggregating clinical data, drafting submission sections, and cross-checking for consistency.
Solution
Deployed a multi-agent pipeline with specialist agents for data extraction, section drafting, cross-reference validation, and formatting — coordinated by a project manager agent.
Tech Stack & Tools
LangGraph
AutoGen
CrewAI
Model Context Protocol
LangChain
Redis
OpenTelemetry
AWS / Azure / GCP
Get Started
Solve your most complex workflows with AI agent teams. Talk to a21 about multi-agent systems.
