DigiEdze AI · Multi-Agent Systems
Build AI Teams Before AI Assistants
Combine specialized agents into coordinated systems where each agent has a defined role, its own permissions, tools and a clear objective, supervised by an orchestrator that owns the outcome.
- supervisor orchestration
- agent-to-agent protocols
- role-scoped permissions
- shared memory
- termination control
- system evaluation
Reference architecture
- 06Supervisor / orchestrator
- 05Shared context & memory store
- 04Specialist agents — research · analysis · action · verification
- 03Per-agent policy & identity
- 02Tool registry
- 01System evaluator and cost tracker
The Problem
What Changes When This Goes Wrong?
One agent handed every tool becomes unpredictable and impossible to evaluate.
Broad permissions on a single identity make least privilege unenforceable.
When several agents share a workspace with no protocol, they duplicate, conflict and loop.
We Build
Multi-Agent Systems Capability Grid
Role decomposition
Splitting a workflow into specialised agents — researcher, analyst, executor, verifier — each independently testable.
Orchestration patterns
Supervisor, sequential, parallel and debate patterns selected against the task, not applied by fashion.
Per-agent identity
Each agent gets its own workload identity, credentials and permission scope, so least privilege is real and revocable.
Shared context protocol
A structured hand-off contract between agents, so context transfers without an ever-growing prompt.
Conflict and loop control
Termination conditions, step budgets and arbitration rules that stop runaway coordination.
System-level evaluation
Scoring the outcome of the whole system, not just each agent's turn.
How it works
Tech Stack Behind Multi-Agent Systems
From cross-collaboration to interoperability, our agents talk to each other across workflows and enterprise systems, working in sync to get your business use case done end to end.
- LangGraph and equivalent orchestrators
- A2A protocols
- MCP
- Workload identity
- Distributed tracing
- Message queues
Use Cases
Where this Lands in Practice?
Security Operations
Triage, enrichment, containment and audit agents work one incident with separate privilege levels.
Supply Chain
Demand, inventory and logistics agents negotiate a plan and surface the trade-off for a human to approve.
Engineering
Specification, implementation, review and test agents move a change through a governed pipeline.
Outcomes
What We Measure
Every deployment is measured against the business metric it was built to move, from cost saved to revenue unlocked.
- End-to-end system success rate
- Steps per completed task
- Cost per workflow
- Handoff failure rate
Questions
Multi-Agent Systems, Answered Directly
When is a multi-agent system the wrong answer?
When one well-scoped agent with good tools would do. Multi-agent designs add coordination cost, latency and failure modes. Introduce a second agent when a role genuinely needs different permissions, a different model, or independent evaluation.
How do you keep coordination costs under control?
Step budgets per agent, token budgets per workflow, structured hand-off contracts instead of transcript passing, and cost tracked per agent so an expensive coordinator is visible immediately.
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Explore Multi-Agent Architecture
Thirty minutes with an architect who has built this. Bring the constraint that has stopped you — data, security, cost, sovereignty, agents or legacy systems.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT