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Eight Specialist Agents That Screen a Data-Centre Site in One Run

Pre-due-diligence on a candidate site used to mean weeks of separate specialist reviews in incompatible formats. We built a swarm of eight domain agents that queries real public data and assembles one scored, sourced report.

European energy major · data-centre development

energy
8
specialist Agents
19
report Sections
4
weeks To V1

The Need

Screening a candidate co-location site before due diligence means assessing land, transport, grid, environment, water, planning and fibre — each by a different specialist, in a different format, on a different timeline. Sites could not be compared on a like-for-like basis, so early ranking came down to judgement. Worse, community acceptance and ESG — the dimension most likely to kill a project late and expensively — was assessed last, or not at all, because it was the hardest to quantify.

The Approach

We built a swarm of eight specialist agents on the Claude Agent SDK, one per diligence domain, with community and ESG promoted to a first-class agent rather than an afterthought. Each agent queries real public sources — cadastral registries, open infrastructure and grid maps, OpenStreetMap, the European environment agency's protected-area service, internet-exchange peering data and crawler-wrapped government portals — instead of relying on model recall. Every agent output, its evidence and its score persist to their own rows, and a narrative assembler composes them into a single structured report. Eight golden-site fixtures run as graded evaluations in CI, so a prompt change that quietly degrades report quality fails the build rather than reaching a decision-maker.

Technologies Used

Claude Agent SDKTypeScriptSupabaseReactVitestGitHub ActionsFirecrawl

The Output

—Eight specialist agents — land, transport, grid, environment, water, planning, fibre, and community/ESG
—A 19-section pre-due-diligence report per run, with a composite score and per-agent scores and observations
—Over 40 ranked next-step actions carried into the following diligence stage
—Every agent output, evidence item and score persisted and auditable after the fact
—Eight golden-site fixtures graded on every CI run, so quality regressions fail the build
—An auth-gated report viewer rendering live engine output, not static slides

The Impact

—Version one delivered in four weeks, from empty repository to reports a decision-maker reads
—On the benchmark site the engine scored 63.05 against an independent engineering firm's reference figure of 62 — roughly one point apart, with no analyst in the loop
—Scores discriminate rather than cluster: across eight reference sites results span 47.96 for a rural location to 75.5 for a prime corridor
—Community and ESG risk is now scored on the first pass, at 63.6% differentiator coverage, instead of surfacing after money is committed
—Site comparison moved from incompatible specialist documents to one consistent, sourced score

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