Solutions
Problems, solved by the stack
Each of these is a real problem we have hit building agent infrastructure. Each one is solved by a combination of LatticeAG products - open-source cores you can self-host, with hosted tiers where speed matters.
The reference architecture
See the whole stack in one agent
Every problem above is also part of one bigger story: what happens when every applicable LatticeAG product is wired into a single agent. Eight layers, from context to training data.
The Perfect AgentBy problem
Pick the problem, get the stack
Autonomous agents can't be trusted with tools
Gate every tool call before it executes, verify every result after, and keep a human in the loop for the decisions that matter.
A single out-of-policy tool call - sending an email with a leaked key, deleting a production resource, overwriting the wrong file - can cause more damage than any human can intervene. Pre-execution guardrails alone can't catch correct calls with wrong results, and post-hoc review alone can't stop damage that already happened.
The LatticeAG stack
Outcome: Every tool call is classified and gated before execution (LexShield), every result is verified in milliseconds after execution (LexVerdict), high-stakes actions queue for durable human approval (VekInbox), and the beliefs behind each decision are inspectable (Axion).
Agent behavior is a black box
Record, replay, diff, and inspect what agents actually did - and why.
Log greps only show what was logged, re-runs produce different paths, and reasoning traces vanish when a session ends. Teams debug by guessing, and the belief that caused a bad action is gone by the time anyone looks.
The LatticeAG stack
Outcome: Axion reads what an agent believed from its own output in real time, VisReplay records and replays full sessions deterministically, VisCompile diffs behavior against a pinned baseline before deploy, and VisBoard gives agents a persistent shared workspace.
Agents have no memory and burn context re-reading everything
Give agents structured, provenance-tracked project context that survives sessions.
Every session starts from scratch: agents re-read the README, scan the directory tree, and reconstruct a mental model of the project - wasting context tokens and silently acting on stale context as the codebase drifts.
The LatticeAG stack
Outcome: AxiContext maintains a SQLite Context Graph and PROJECT_CONTEXT.md with drift detection, while VisBoard gives agents versioned notes, checklists, and files that persist across sessions.
Single models hallucinate and confidence is not correctness
Replace one model's confident guess with adversarial multi-model consensus.
When a wrong answer costs real money, reputation, or safety, betting on one model's opinion is a risk. Naive multi-agent systems run the same model in parallel, so the same blind spots repeat in every answer. There is no formal way to decide which of several competing solutions is actually best.
The LatticeAG stack
Outcome: PolyBrain decomposes objectives across role-specific models with citation enforcement and claim verification, and PolyGnosis runs adversarial consensus - independent solve, critique, RRF + Borda scoring, and a quality gate - to converge on mathematically defensible answers.
Agents can't talk to each other
Let agents on the same machine or LAN discover each other and delegate tasks - no cloud required.
Hermes, Codex, Claude Code, Cursor, and custom agents each speak their own protocols. Internet-scale standards need DNS, TLS, and public endpoints, so local agent meshes don't exist outside of demos.
Outcome: PolyMesh gives agents a local-first protocol for capability discovery and task delegation over WebSocket or Unix sockets, while PolyBrain orchestrates role-based multi-model workflows on top.
Release notes are manual, stale, or fabricated
Draft release notes from real git history and publish after human review.
Hand-written notes miss PR context and drift from reality; generic AI changelogs fabricate from a model's guess instead of the repo's actual commits, PRs, and diffs. CHANGELOG.md and GitHub Releases fall out of sync, and every release repeats the chore.
The LatticeAG stack
Outcome: PolyScribe ingests commits, PRs, and diffs for a ref range, drafts structured notes by contribution type, and supports a full draft, review, edit, publish workflow - with --json for CI.
Training agentic models on broken synthetic data
Build structurally-perfect training traces, correctness by construction.
Most distillation frameworks ask a teacher to demonstrate agentic behavior end to end - and most models can't, producing fabricated tool arguments, malformed JSON, and shallow one-call trajectories that fail their own checks.
The LatticeAG stack
Outcome: ForgeDistill builds deterministic tool-call chains with unskippable dependencies, spends teacher tokens only on prose, and gates every trace for format and grounding - verified at 100% gate pass rates.
The full stack
Six series, one philosophy
Every LatticeAG product belongs to one of six series - Poly (multi-agent orchestration), Lex (edge AI infrastructure), Vek (agent as an API), Axi (agent context and memory), Vis (workspaces and observability), and Forge (the model factory). Together they cover the full lifecycle of building, running, inspecting, and improving agentic systems.
6 series across 19 products. The pattern is consistent: open-source the core for trust, keep the hosted backend invite-only because speed is the moat.
Start anywhere
Not sure where to begin?
Browse every product by series, or jump straight into the ones that ship as open source today.