Home/Compare/Aedeon vs AI coding agents
Architectural comparison · Enterprise platform
Why autonomous single-repo agents break at the enterprise boundary.
Verified production benchmark · The New York Times
The New York Times evolved PLATO off Oracle 12c onto AWS Aurora PostgreSQL with Aedeon: six million lines of code in twelve weeks, verified against production traffic before it shipped.
01 · Executive framing
Enterprise legacy systems do not live inside an isolated GitHub folder. They span distributed message buses, batch schedules, stored procedures and heterogeneous services written across decades. Autonomous coding agents attempt to solve software engineering by placing an LLM inside an iterative prompt-and-bash loop. That approach can draft single-repo pull requests, but it breaks at the enterprise boundary, where cross-system dependencies govern production stability.
02 · The three architectural pillars of Aedeon
System visibility
15entity kinds16relationship kinds
Single-repo agents inspect only the files mounted in their active workspace.
Aedeon constructs a living graph that maps services, databases, schema definitions, RPC contracts, message queues and batch workflows across your entire software estate.
Planning precision
Supervisorproprietary planning model
Single-repo agents rely on unconstrained trial-and-error prompting that burns tokens while wandering through unfamiliar architectures.
Aedeon uses Supervisor, a dedicated proprietary planning model trained specifically to produce deterministic, multi-step evolution roadmaps.
Verification standard
Realproduction traffic, replayed
Single-repo agents stop once local synthetic mock suites pass, whether or not those tests reflect production.
Aedeon captures and replays actual production traffic through the transformed components, proving they behave the same under true operational load.
03 · Side-by-side capability matrix
| Architectural capability | Autonomous coding agents | Aedeon platform |
|---|---|---|
| Operating scope | Single repository or local workspace | Distributed multi-repository and multi-database architectures |
| Knowledge representation | Temporary in-memory prompt context | Persistent Code Intelligence Graph (15 entity kinds, 16 relationship kinds) |
| Heterogeneous legacy support | Reads what is in the open repository; no view of the systems around it | Comprehensive: COBOL, PL/I, SAP ABAP, Oracle Forms, Java, .NET |
| Blast radius analysis | None; localized file search only | Semantic blast radius calculated across every consumer in the graph |
| Planning and reasoning | Prompt-driven trial-and-error agent loops | Dedicated proprietary Supervisor planning model |
| Verification standard | Synthetic unit tests and mocks | Dual-run production traffic replay against the legacy system |
| Commercial model | Variable usage metering and token burn | Evolution credits per change, with the estimated cost shown before you approve |
| IP and governance | Depends on the vendor’s hosting and data terms | Runs in your cloud; never trained on your code |
04 · Real-world architecture scenario
When migrating a core enterprise billing engine off an Oracle 12c monolith, autonomous agents break because they cannot inspect stored procedures or predict downstream locking behavior. Aedeon ingests schemas, procedures and batch workflows into the Code Intelligence Graph, generates a phased decomposition roadmap with Supervisor, and validates every transaction through live traffic replay.
Not seen
Each one is an outage, an audit finding, or a quarter of cleanup — discovered in production.
Evolve plan
With Aedeon the same change runs against the graph. Graph had already mapped the stored-procedure rule, the reconciliation job, the database-layer checks and the cross-system lineage — because it reads all the code, not one repo. Supervisor planned all of it from one sentence of intent. Evolve performed the change and verified the new system against the old on production traffic.
05 · Frequently asked questions
06 · Next steps and platform access
Sign up on Aedeon to map your enterprise systems, calculate the true dependency blast radius, and run verified legacy modernizations, with every change proven against production traffic before cutover.
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