Field Operations Agent

An offline agent for crews with procedures, asset history and notes.

Industry	Level	Difficulty	Recommended mode
Energy & utilities	4 of 5	Expert	sq8

Coming soon

A complete, runnable Field Operations Agent reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Crews work in remote areas.
• Asset history is in the office.
• Storm response is chaotic.

How SrvDB powers Field Operations Agent

1. Asset records, procedures and notes in separate databases on tablets.
2. The agent recalls asset history offline.
3. Notes synced later.
4. Crash-safe.

What changes

Today	With Field Operations Agent on SrvDB
Calling the office	Local answers
Paper notes	Searchable notes
Chaotic response	Shared knowledge

How it works

1. Preload databases.
2. Use offline.
3. Capture notes.
4. Sync.
5. Review.

SrvDB features it uses

• Several databases: each database is a directory, so different data types or tenants stay separate.
• Fully offline: SrvDB runs in-process and never opens a network connection.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• SQ8 compression (sq8): stores each value in one byte, 4× smaller than full precision.

Use cases

• Storm response
• Asset maintenance
• Meter issues
• Safety checks
• New-crew support
• Substation visits

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./field-operations-agent-db", dimension=384, mode="sq8")

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def asset_recall(question, k=5):
    hits = db.search(embed(question), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Safety procedures follow official rules.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Energy & utilities projects

Level	Project	What it does
1 · Beginner	Procedure Finder	Operators find the right procedure from a description.
2 · Intermediate	Outage Analyzer	Finds similar past outages and their causes.
3 · Advanced	Inspection Image Matcher	Matches drone and field inspection images to known defects.
4 · Expert	Field Operations Agent (this page)	An offline agent for crews with procedures, asset history and notes.
5 · Frontier	Grid Analog Forecaster	Finds historical analogues of current weather and load patterns.

See also: Use cases · Live examples (coming soon)