# Scientific Research Agents — AI Architecture

A Senior AI Engineer building autonomous AI systems for scientific research automation at a US-based biotech client. Custom MCP servers, RAG pipelines over scientific corpora, and a held-out evaluation suite.

- **Author:** Fedor Chishchin
- **Role:** Senior AI Engineer
- **Period:** 2025-03 → present
- **Stack:** Python · FastAPI · Claude Opus · MCP · pgvector
- **Source:** https://fedorthinks.com/work/scientific-research-agents

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## The problem

Scientific research is dominated by a small set of repeatable but cognitively
expensive workflows: corpus search across paywalled literature, structured
data extraction from heterogeneous PDFs, hypothesis generation from prior
results, experimental-design critique. Each of those is well within reach of
modern LLMs — but only if you build the surrounding architecture correctly.

A naive single-prompt approach falls apart fast: corpora are too large for
context windows, the right tools are domain-specific (chemistry-aware
literature search is not Google), and any answer that ends up in a research
note has to be auditable.

## What I architected

**Multi-agent orchestration with a hierarchical planner–executor split.** A
planner agent decomposes a research request into typed sub-tasks; executor
agents handle the actual tool calls (literature search, data extraction,
hypothesis ranking). The planner stays inside a tight context budget; the
executors can fan out without polluting it.

**Custom MCP servers as the only tool surface.** Three transports in
production (Stdio for local dev, SSE for streaming results, Streamable HTTP
for the production fleet) — all servers I built end-to-end. Tools are
versioned and discoverable, agents never see raw HTTP, and the same MCP
contract powers internal evals and live runs.

**An evaluation framework that the agent never sees during development.**
Public agent benchmarks for cross-system comparison plus a larger custom
scenario suite drawn from real biology workflows, held out during
development so improvements are measured, not declared.

**Specification-driven development.** Every agent has a written spec — system
prompt, tool list, guardrails, expected output schema — checked into the
repo. The spec, not the prompt, is the artifact reviewers approve. New
behaviors land via spec PRs.

## Day-to-day reality

I do not type the implementation. I direct coding agents (Claude Code on
Opus) as the implementation layer while owning the architecture, the spec
discipline, and the evaluation discipline. Twenty years of production
judgment go into deciding what should be built; agent practice gives the
velocity to actually ship it. Spec quality is the new bottleneck.

## Outcome

A working, evaluated, production-bound multi-agent system that automates
research workflows previously done by hand — with measurable improvement on
the held-out benchmark set, not just demo-quality screenshots.

The architectural decisions documented here are the basis for everything
else I'm building right now.