AI coding agents are getting very good at editing files, running tests, and opening PRs.
After heavily using tools like Cursor, Claude Code, and GitHub Copilot, I noticed they all share the same core limitation:
They have no persistent understanding of your system.
Ask the same question next week and they:
So instead of building another coding agent, I built the layer underneath them.
ASIL (Engineering Intelligence Infrastructure) is a persistent, temporal, causal knowledge graph for software systems.
It connects:
into one queryable system that any AI agent can access through MCP.
The goal is simple:
Stop making AI agents rediscover the same engineering knowledge over and over again.
Most coding agents understand:
ASIL understands:
Instead of:
“GPT thinks this caused the outage”
ASIL derives causal chains from observable system state:
Every conclusion includes:
No black-box “AI intuition.”
uv run asil ask "How does auth work in this repo?"
ASIL combines:
to return:
uv run asil replay INC-2026-04-12
ASIL reconstructs:
as a dependency-aware replay graph.
Think:
Time-travel debugging for distributed systems.
uv run asil drift report
ASIL learns expected dependency boundaries and flags:
before the PR merges.
ASIL exposes 13 MCP tools usable from:
The agents become clients of the intelligence layer.
ASIL stores every verified engineering conclusion in persistent memory.
When someone asks a semantically similar question later, ASIL can reuse the prior verified reasoning instead of re-running the full LLM pipeline.
On cache hits, the cost drops close to:
just the embedding lookup
Repeated engineering queries become dramatically cheaper over time — especially across teams.
ASIL does not let the LLM invent causality.
That rule shapes the entire architecture.
Causal links come from deterministic signals:
The LLM consumes evidence.
It does not fabricate it.
That distinction matters once AI systems start participating in production engineering workflows.
Everything runs locally:
No central server.
No telemetry.
Your graph stays yours.
The only optional network dependency is the reasoning LLM.
Most AI tooling is racing toward:
“make the agent better at editing code”
I think the more important problem is:
“give agents persistent engineering intelligence”
That means:
That’s the layer ASIL is trying to build.
Built solo over 6 months with:
Python, FastAPI, Neo4j, Qdrant, Postgres, Tree-sitter, Next.js, Tailwind, ReactFlow, and MCP tooling.