iBoost/Portfolio/EP Agent

AI agents·2026 — now

EP Agent

An agent that reads commercial briefs like a seasoned executive producer: it finds the central contradiction, flags what is unsolved — and goes quiet when it is not sure.

Our role

Product, architecture, pipeline, deployment

Status

Running in production

Period

2026 — now

Platform

Web · client’s internal perimeter

AI AGENTS EP Agent 01Brief upload02Parsing andquoteextraction03Evidencecheck andgates04Judgement orabstention05Deck andback-channel PYTHON 3.14 · FASTAPI · POSTGRESQL 18 · PGVECTOR · CLAUDE API · FASTEMBED · JINJA2 IBOOST.UA · 2026 — now

System map · internal perimeter, access under NDA

01

What it is

The client is a US production company receiving dozens of briefs a week. The task is not “write a summary” — any model can summarise. The task is to reproduce a senior producer’s judgement: to see what the brief’s real contradiction is, which beat carries the whole idea, what the document is missing, and what should be asked out loud on the first call.

The core engineering idea is not in the prompt, it is in the gates.

Every claim the agent makes must carry verbatim quotes from the brief; the quotes are verified mechanically; unverifiable ones are dropped. If core claims fall out during that check, the agent is forced to abstain rather than invent. That is a rare property — a system that can say “I don’t know”.

02

capabilities

What it does

01

Brief reading

Audience target, central contradiction, load-bearing beat, flagged unsolved items and missing information — plus a draft in the client’s voice.

02

Back-channel plan

Project stage identified by a classifier, then what to learn, who to call, what to ask out loud and what to listen for.

03

Insight test

When a proposed angle is supplied, the agent stress-tests it through a dedicated procedure.

04

Any file accepted

Multi-file upload: PDF, pages, docx, audio and video — the latter routed to GPU transcription in a neighbouring system.

05

Method as a container

The client’s proprietary method is versioned in the database as separate sections. Until filled in, the system runs in scaffold mode and says so plainly.

06

Evaluation harness

Held-out briefs, six criteria, a versioned eval run: a prompt change shows up in numbers rather than in vibes.

03

architecture

How it works

01

Brief upload

02

Parsing and quote extraction

03

Evidence check and gates

04

Judgement or abstention

05

Deck and back-channel

Under the hood

  • Python 3.14 + FastAPI, Postgres 18 with pgvector for nearest-exemplar retrieval.
  • Strict JSON schemas on structured outputs: the model cannot return a shape nobody expects.
  • The agent’s constitution lives in its own module with a prompt version — a change is recorded, not dissolved into history.
  • Exactly one uvicorn worker: background tasks assume a single process, and that is written into the ops notes.
  • After a restart, hung jobs are automatically marked failed — the queue never lies about its own state.
  • Nothing leaves the system: confidentiality is a client requirement, stated in the interface itself.
Python 3.14FastAPIPostgreSQL 18 · pgvectorClaude APIfastembedJinja2

04

scale

Numbers

0

claims without quotes

6

evaluation criteria

2

models: reading and auxiliary

NDA

data handling mode