Multi-agent systems that run real business workflows end to end.
Planner, worker and reviewer agents with tool access, memory and human-in-the-loop, orchestrated, observable, and safe to run unattended.
What is a multi-agent system?
A multi-agent system is a set of specialised AI agents, planners, workers and reviewers, that coordinate to complete a multi-step business workflow using your tools and data. Eazyware designs, builds and deploys these systems with durable orchestration, memory, permission guardrails, human approval gates and evaluation suites so they can run unattended in production.
| Service line | AI Agents & Automation |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $24,500 |
| Typical range | $24,500 – $84,000 |
| Deliverables | 5 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
A single prompt cannot process an insurance claim, reconcile a ledger, or qualify and route 500 leads a day. Those are multi-step workflows with tools, checks and exceptions.
How do we approach it?
We begin with the workflow, not the model. The first two weeks map the process as it is actually done: every step, decision, exception and tool, and who is allowed to do what. From that map we design the agent topology, usually a planner with specialised workers and a reviewer, and decide which steps may be automated, which need approval, and which stay with people. Tools are built as scoped, permission-checked functions over your systems, never as open database access. The orchestration layer gives every run durable state, retries and a trace, so a failure at step six resumes at step six. Agents run in shadow mode first, proposing actions that humans approve, and gain autonomy intent by intent as the evaluation evidence accumulates.
What do clients use it for?
- Lead qualification and routing across CRM and email
- Order-to-cash and invoice exception handling
- Claims triage with document reading and rules
- Research, report assembly and approval workflows
- Vendor and employee onboarding across systems
Is it the right fit?
Good fit when
- Operations teams with high-volume multi-step processes
- SaaS products adding automation as a feature
- Enterprises replacing brittle RPA
Probably not when
- Single-question chatbots
- Processes with no digital inputs or tools
What do we build?
- Agent architectures: planner/executor, supervisor, hierarchical, swarm
- Tool and API integration with CRM, ERP, email, databases and internal APIs
- Memory: short-term, long-term and shared state
- Human-in-the-loop approvals and escalation paths
- Policy and guardrails: allowed actions, budgets, rate limits
- Durable orchestration with retries, tracing and cost dashboards
What you get
- Agent system deployed in your cloud
- Tool connectors
- Admin console
- Eval suite
- Runbooks
How does the engagement work?
- 01
Workflow mapping
- 02
Agent design
- 03
Tool integration
- 04
Shadow mode alongside humans
- 05
Gated autonomy
- 06
Full run with monitoring
What does good look like?
A system that completes the workflow end to end for the common cases, escalates the uncommon ones with a summary, and can be audited step by step for any run. The operations team sees a dashboard of tasks completed, tasks escalated and why, cost per workflow and eval scores; the security team sees an audit log and a permission model they signed off; and the business sees hours returned to the people who used to do the routine part.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Agent projects fail on scope and on trust. Trying to automate the whole process at once produces an agent that is wrong often enough that nobody trusts it for anything; we automate the well-defined majority and route the rest. Giving agents broad tool access produces incidents; we give them narrow tools with budgets and approval gates. Skipping evaluation produces regressions when models change; we run the eval suite on every prompt and model update. And forgetting the humans produces a system nobody uses; the escalation experience is designed as carefully as the automation.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Tasks completed autonomously vs escalated
- Human hours removed per week
- Error rate against the eval suite
- Cost per completed workflow
Which technologies do we use?
- LangGraph
- Temporal / Inngest
- OpenAI / Anthropic / open-weight
- Node.js
- Postgres / Redis
- Langfuse
Who does the work?
An AI engineer specialising in agent orchestration, a backend engineer for tool integration, an architect for the permission and audit model, and a delivery lead working directly with your operations owner.
What do you need to bring?
A process owner who can walk us through the workflow including the exceptions, API or database access to the tools the agent will use, historical examples of completed work for the eval set, and a view on which actions must always have a human approve them.
Frequently asked questions
How do you stop agents doing something wrong?
Scoped permissions, approval gates, spend limits and a full audit trail. Agents earn autonomy in stages.
Can it use our internal tools?
Yes, via APIs, database access or MCP connectors.
Which model?
Whatever benchmarks best on your task. Most systems route between two or three.
Where does this fit?
Multi-Agent Systems & Workflow Orchestration is part of our AI Agents & Automation line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.