+51%
more PRs per developer with AI assistance
DORA 2025
Agent tools, not another agent.
Deterministic tooling built on your code’s genome changes what agents can do. Instead of grepping and guessing, agents search by type and symbol, arrive with the context already loaded, and apply fixes as rules that land the same way everywhere. Every change stays coordinated and auditable. The result is agent output that’s faster, more precise, and more token-efficient across your entire codebase.
Works with the coding agents you already run
- Claude Code
- Cursor
- GitHub Copilot
- OpenAI Codex
- Windsurf
- Cline
Delivered through the Model Context Protocol to any MCP-compatible agent, available in CLI and SaaS.
The Shift
The agent’s job is to reach for the tool, not to be it.
Approximation is fine for one file. At scale, the agent should plan, and Moderne is the deterministic harness that sits between the agent and your code, doing the structural work on the genome the same way every time.
Moderne sits between your agent and your code
Agent
Moderne
Code
Skip the harness and that approximate output lands on a human reviewer. Writing code accelerated, reviewing it didn’t, and at scale the review queue becomes the bottleneck.
+54%
more bugs per developer with AI assistance
DORA 2025
66%
of devs frustrated by AI that’s “almost right, but not quite”
Stack Overflow 2025
The Loop
Building an efficient, validated loop.
Trusted change runs on a loop, not a prompt. Four deterministic steps (find, frame, fix, govern) strip out work that the agentic loop otherwise wastes.
Moderne Trigrep symbol-aware code search
Locate the pattern, everywhere it lives.
Match on types, signatures, and inheritance to surface every real occurrence and no false ones, across the whole estate, in milliseconds. Trigrep’s trigram index is built from the LST, so the first search is the final answer and no follow-up reads are needed.
- Milliseconds Queries at any repo size
- 1B+ Lines, constant-time
- 0 Follow-up reads
Moderne Prethink precomputed architectural context
Understand it with certainty, before the session starts.
Prethink runs CPU-only static analysis ahead of the agent, so pre-computed resolved context (architecture, dependencies, code quality, testing gaps) is ready before the agent begins. The agent stops paying a token tax on discovery every session.
- 0 Discovery tokens at runtime
- 5s vs ~2 min agent alone
- CPU No LLM inference
Recipes on the Lossless Semantic Tree deterministic transformations
Capture the change as one deterministic recipe.
A precise, reviewable rule that excises the pattern everywhere the same way every time. Where an inference-only agent burns millions of tokens guessing, one recipe lands the same exact edit in every repo, whether you have one or a hundred thousand.
- ~2,000× fewer tokens than agent alone
- 100% completion (vs 25% inference-only)
- 7,200+ Recipes ready now
Moderne Changelog & Transcripts change control and session capture
Every change managed together, every session auditable.
Changelog overlays your SCMs (GitHub, GitLab, Bitbucket, internal) with one portfolio-wide view and bulk approve, merge, and close. Transcripts captures every agent session so learning compounds for agents and humans alike. What changed, who approved it, and why becomes provable across the whole estate.
- 1 view across repos and SCMs
- Bulk approve, merge, close at scale
- All sessions, provable
The Substrate
The substrate your agent navigates.
As raw text, code is unstructured for a machine. As a Lossless Semantic Tree, every type, signature, and call relationship is resolved into a substrate the agent reads the way a ribosome reads DNA, built once and kept current as the agent edits.
-
01
Sequence
Each repo becomes its LST.
Compiler-accurate, format-preserving, and type-attributed: a complete model of what the code is, not a guess at what it looks like.
-
02
Read
The agent navigates instead of inferring.
The agent sees resolved types, real call paths, and exact relationships, so every match is a real match instead of a regex hit that needs three more reads to confirm.
-
03
Act
Recipes drive the change.
The recipe is precise, reviewable, and idempotent. The agent plans, the recipe executes, and the result is the same in repo 1 and repo 100,000.
// raw text
user.save(data)
// resolved on the LST
User user // com.acme.identity.User
data: SignupPayload // record, 7 fields
.save(SignupPayload) // returns UserRecord
// throws DuplicateUserException
// called from 412 sites
✓ ready for the agent
Proof
Why the agent can’t do this alone.
Even capable models hit a wall on this. The problem isn’t intelligence, it’s structure: an inference-only agent handed a portfolio-wide migration has to fit a complex shape using whatever it can scrounge from text. Discovery alone burns most of its budget, costs swing wildly run to run, and it never quite lands the edit. A bigger model doesn’t fix that, but giving the agent the genome and a deterministic recipe to reach for does.
~60%
of an agent’s tokens go to discovering the code before any real work begins
2.5%
signal-to-noise: of 20K tokens pulled in, only ~500 matter
30×
cost variance across runs of the same task
Java 8 to Java 25, same task
| Pure LLM | Agent + Moderne recipes | |
|---|---|---|
| Tokens | 61,000,000+ | 30,000 |
| Time | 45 min | ~3 min |
| Completion | 25% | 100% |
| Reproducibility | Variable per run | Deterministic |
| Review surface | Diff-by-diff | One recipe |
~2,000× more token-efficient.
At Scale
Why this matters beyond one repo.
Detection is commoditized, remediation isn’t. The next CVE, the next framework EOL, the next regulator change: they’re cheaper than ever to find. Fixing them precisely at fleet scale is where Moderne sits.
One exact change, not a million guesses.
The recipe runs against the LST, so the change is exact every time and not approximate. Hand-fixing each occurrence is a million independent guesses, each a chance to drift.
One repo or 100,000, identical.
Because the change is deterministic, it’s safe to multiply. The same recipe lands the same way in every repo, with no per-repo drift and no compounding error.
One recipe to review, not a thousand diffs.
The recipe is precise and reviewable by design, so a human stays in control at the point it matters: before it ships, not in the cleanup after.
Moderne reduces Java upgrade time by approximately fifty percent.
Gartner Peer Insights
300+
repos changed in a single governed operation
5 sec
architecture context retrieval (vs ~2 min agent alone)
32–70%
token reduction on document generation tasks
Use Cases
What teams actually do with this.
Five categories cover the work most engineering orgs are running on Moderne today, where humans lead, agents operate, and Moderne keeps the change deterministic.
Govern coding agents
Audit and control agent-driven change across the estate, with every session captured.
Explore
Accelerate coding agents
Drop discovery and migration tokens by 1000×+ and cut cost variance and runtime drift.
Explore
Platform migrations
Java, Spring Boot, .NET, language and runtime moves at portfolio scale.
Explore
Tech debt reduction
Continuous, governed, and measurable: codify the fix once and ship it everywhere.
Explore
Security remediation
One CVE, one recipe, every service, all auditable from finding to merge.
Explore
Multi-repo coordination
Synchronized changes across SCMs with full traceability, including bulk merge and bulk close.
Explore
Run the benchmark
See the 61-million-token difference live.
Run the Java migration benchmark on your codebase. We’ll show you what Trigrep, Prethink, and the recipe library do for your specific agent stack.
Questions
Agent tools,
answered.
For one repo, yes. Across your whole engineering org, no, not without burning enormous token budgets on tasks an LLM was never designed for: deterministic, fleet-wide structural change. Moderne is the harness underneath the agent so the work scales, and your agent stays where it is.
It shifts work from GPU inference to CPU precomputation. Trigrep eliminates follow-up reads with symbol-aware search, Prethink generates context before the session begins, and for migrations, one recipe replaces hundreds of thousands of tokens of agent guesswork. A Java 8 to 25 migration drops from 61 million tokens to 30,000.
Moderne doesn’t replace your agent, it works alongside Claude Code, Cursor, Copilot, Codex, Windsurf, Cline, and any MCP-compatible agent. The agent you use today gets faster, cheaper, and more reliable without switching tools.
Through the Model Context Protocol, available in our CLI and SaaS product. You connect it like any other MCP server.
Trigrep is a symbol-aware, trigram-indexed code search built for agent workflows at enterprise scale. Its index is built from the LST, so results include type and symbol data. When an agent searches for a class or symbol, Trigrep returns semantically precise results in under a second with no follow-up reads required.
Prethink is a precomputed context layer that runs before an agent session. CPU-only static analyzers generate structured knowledge about architecture patterns, dependency graphs, and service boundaries, then make it available to the agent as context.
Recipes are deterministic, LST-powered transformations that agents call instead of writing ad-hoc code. Rather than grepping, reading, and editing thousands of files, an agent reaches for one of more than 7,200 recipes that execute correctly and consistently at scale.
Through governed, synchronized, deterministic execution across thousands of repositories at once. Organizations like Boats Group have executed changes across 300+ repositories in a single operation with full audit trails and no partial states.