Three Ways Teams Ship
Lattice in Production.
Different industries. Different agents. Same shape underneath: SQL where the source of truth lives, structured context where the agents read.
Microservices, mapped.
One context DB across 50+ services.
The Problem
A banking-stack engineering team was maintaining one CLAUDE.md per microservice — fifty stale context files, all drifting behind the real code. Every CI agent re-read the world on every session.
The Lattice Fix
Lattice owns the service registry: ownership, dependencies, migration history, and per-service docs all live as tables. Render specs produce one context directory per service. Every CI agent — Claude, Codex, internal — reads from the same source. When service A’s schema migrates, every agent reading service B sees the new shape on the next session.
Outcome
- Onboarding new agents went from "paste in everything" to "point at the DB"
- 3× lower cost per task vs cold-start exploration
- Drift goes to zero — the DB is the source, the docs are the projection
The Shape
“We dropped a markdown file per microservice and replaced it with one Lattice DB. The bills went down. The wrong answers went away.”
The learning loop.
Agents that get smarter without a human curator.
The Problem
An in-house agent platform was running dozens of jobs a day — extraction, QA, ops. Each agent produced learnings ("this contract clause means X"; "this customer prefers Y format") that vanished after the session. The next agent on the next job started from zero. Drift was the default state.
The Lattice Fix
Lattice manages the memory and observations tables that agents write to. Skills, playbooks, and feedback are first-class rows. Agents read them as context on every session and append new rows when they learn something. A reward column lets the platform prune low-value entries automatically. Bidirectional sync means humans can still edit the rendered files, and edits flow back to the DB.
Outcome
- Agents share lessons across sessions and across jobs
- Memory grows with use — no manual curation required
- Stale or low-value memories auto-prune via reward tracking
“We stopped writing playbooks by hand. The agents write them. Lattice keeps them coherent.”
Custom agents at scale.
Tailored AI assistants for every role, one shared knowledge base.
The Problem
Twenty-plus simultaneous projects across fifty-plus staff. Multiple roles, each with different context needs. Org data — curriculum, contacts, policies — had to stay consistent across every agent. Manual classification was the bottleneck.
The Lattice Fix
Lattice auto-tags people, projects, and meeting subjects from emails, transcripts, and Slack messages. No manual classification, no per-document curation. Each role gets a render spec that produces a focused context slice — but the underlying entities are shared. Staff query Lattice in plain language ("what is high-leverage to work on right now?") and the answer is grounded in the team's real data, not the model's guess.
Outcome
- Twenty-plus simultaneous projects, one shared context DB
- Auto-tagged people, projects, and meetings — no manual classification
- New staff fully oriented on day one — they read the same context the team reads
- Plain-language queries return answers that cite underlying entities, not training-data guesses
“My priorities are these projects, I have four hours, what is high-leverage? — and the answer is right. Every time.”
Your Stack Is Next.
The shape works for anything you’d otherwise be hand-curating into prompt context.