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Government · Environmental Regulator

Maharashtra Pollution Control Board

Multi-cloud architecture for agentic AI, blockchain and the dashboards that bring them together.

mpcb.gov.in
Sector
Government, state environmental regulation
Client
Maharashtra Pollution Control Board, India
Engagement
Contracted, in progress
Scope
Multi-cloud architecture for agentic AI and blockchain, and dashboard works
Our role
Architecture support alongside the implementation

The client

The Maharashtra Pollution Control Board (MPCB) is the state regulator responsible for environmental protection in Maharashtra. Established on 7 September 1970 under water pollution legislation and later given responsibility for air pollution control, it regulates industrial compliance through consent management and pollution authorisation, and monitors air, water and noise quality across the state.

A regulator runs on records and readings that have to stay accurate, traceable and available for years. Any system that works alongside that work, AI included, has to meet the same bar. That is an architecture question before it is a model question.

Why one cloud is not enough

Agentic AI and blockchain ask opposite things of the platform underneath them. Agents want reach: the best model for each task, close to the data it needs, with room to scale when work arrives in bursts. A ledger wants independence: no single party, and no single provider, able to rewrite or switch off the record.

Put both on one cloud and that provider becomes a single point of failure for the AI and a single point of control for the ledger. A multi-cloud architecture separates the two. Each workload runs where it is best served, and one shared control layer holds the whole estate together.

Multi-cloud estate for agentic AI and blockchain Three clouds each run AI agents and one ledger node. The ledger nodes are linked into one shared ledger across all three clouds. A dashboard layer sits above the clouds and a shared control layer for identity, networking, logging and policy runs beneath them. Dashboards: agent activity and ledger records in one view Cloud A Agent Agent Cloud B Agent Agent Cloud C Agent Agent Shared control: identity, networking, logging, policy
AI agent workload Ledger node One shared ledger across clouds

What multi-cloud gives each side

The same four concerns, answered once for the agents and once for the ledger.

Agentic AIBlockchain
Resilience When an agent is part of a regulatory process, an outage at one provider should slow the work, not halt it. Agent workloads fail over to a second cloud. If one provider has an incident, nodes on the others keep validating and serving the ledger, so the record stays readable when it is needed most.
Independence No provider has the best model for every job. Each agent calls the model that fits its task, placed by cost and capacity, and can switch without a rebuild. A ledger is only as independent as the infrastructure under it. With nodes spread across providers, no single one can alter, censor or switch off the record.
Data Public-sector records stay in the environment approved for them. Agents reach them through controlled interfaces instead of copying data to wherever a model runs. Entries written to the ledger cannot be quietly changed afterwards, so who submitted what, and when, can be checked by anyone with access.
Accountability Central logging across every cloud records what each agent read, what it decided, what it changed and who approved it. Agents do the reading, checking and drafting; the ledger keeps a permanent record of what was submitted and decided. The AI is fast, and the ledger keeps it accountable.

Our role

Focus20 Labs is contracted to provide multi-cloud architecture support for MPCB’s agentic AI implementation, the blockchain layer and the dashboard works that present the result. We bring the rules we use on every production engagement: guardrails, evaluation and escalation are agreed before launch, and anything that needs judgement stays with a person.

Questions about this work

Why use a multi-cloud architecture for agentic AI?

It lets each agent use the model that best fits its task, keeps work running if one provider has an outage, lets sensitive records stay in the environment approved for them, and records every agent action in one central log regardless of where it ran.

How does multi-cloud help blockchain?

A ledger is only as independent as the infrastructure under it. Spreading nodes across several providers means no single provider can alter, censor or switch off the record, and the ledger stays available through a provider incident.

What is Focus20 Labs doing for the Maharashtra Pollution Control Board?

Focus20 Labs is contracted to provide multi-cloud architecture support for MPCB’s agentic AI implementation, its blockchain layer and the dashboard works that present the result.

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