Reachability Graph Control (rgctl) - AI coding agents default to reading files sequentially. That burns context, misses structure, and produces confident wrong answers about impact and dependencies. rgctl indexes the whole repository once into a rich graph with pre-computed reachability, then serves compact, deterministic query results — so agents (and humans) get the right slice of the codebase without it into the prompt.
If you refactor RESTClientFor
in Kubernetes by hand, you probably do not open a
call-graph tool first. not that I am suggesting you should.. but lets take it as a good example when it comes to impact on the codebase, its dependencies etc. Usually you have an IDE find-references, a mental map of
client-go
, teammates who have been burned before, and CI that will catch what
you missed. For most edits, that workflow is fine.
The landscape changes when you decide to hand the same task to a coding agent. The agent
has a context window, grep, and no tenure on the team and importantly the LLM has its own limits, depending on which one you use.
On a tree with 181,000 functions, asking it to "refactor newPodWorkers
safely" often
produces one of three outcomes: it reads the wrong files, stops at direct
callers and misses transitive impact, or burns most of the turn budget
exploring pkg/kubelet/
without ever reaching cmd/kubelet/main
.
This post is for brave souls who work or plan to work with agents on large codebases. It
explains why rgctl blast radius belongs in
that workflow, what you should expect from the output, and where your existing
tools are still the right choice. The examples use the upstream
kubernetes/kubernetes repository —
cloned locally and indexed with rgctl, not a trimmed or synthetic corpus.
Blast radius answers one question: if I change this function, what upstream code could be affected? It walks the call graph
CALLS
| Output | Meaning |
|---|---|
| Direct callers | |
| Functions that call the target directly | |
| Impact zone | |
| Full transitive closure of upstream callers | |
| Score (0–100) | |
| A capped impact rating derived from both counts |
The score is not PageRank (even though rgctl can also calculate it) and its not guestaming it either. rgctl combines two components:
| Component | Formula | Cap |
|---|---|---|
| Direct callers | direct_count × 25 |
|
| 40 points | ||
| Transitive impact | impact_count × 0.05 |
|
| 60 points |
score = min(direct_component + transitive_component, 100)
A symbol with no upstream callers in the static graph scores 0. A shared
helper with dozens of direct callers and a large impact zone lands in the
40–50 range; scores above 50 flag architectural hotspots where both
fan-in dimensions are high.
Lets take a look at how this is made possible, or as my agent friends always say "lets delve into it.."
Agents work in multi-turn sessions. A live breadth-first search over 1.7M
edges on every "who calls X?" would make that unusable. rgctl does the expensive
work once, at discover
time, and serves lookups from pre-built snapshots.
Step 1 — Build the call graph: discover
parses source, extracts function
nodes and CALLS
edges, and writes graph.snapshot.bin
plus a dedicated
blast_engine.snapshot.bin
.
Step 2 — Collapse cycles: Mutual recursion is common in real codebases
(7,097 circular call dependencies in Kubernetes). rgctl runs Kosaraju's
algorithm on the CALLS
subgraph and condenses each strongly connected
component (SCC) into a single node, producing a directed acyclic graph (DAG).
Cycles no longer break reachability analysis.
Step 3 — Precompute reverse reachability. On the condensed DAG, rgctl
propagates reachability in reverse topological order, storing the result as
dense bitsets — one per SCC. A query becomes a bitset read: O(1) lookup
on the condensed graph, not a per-request graph walk.
Step 4 — Serve through tiers: At query time, rgctl tries the fastest path
first:
macro_call_index.db
)--with-slices
or
--policy-file
)On Kubernetes that means a ~13 s one-time index, then ~1 s per symbol
query — no daemon, no remote service. Re-run discover
after large merges so
the graph stays current. The full breakdown is in the
A typical agent turn looks like this:
You: "Change how newPodWorkers handles static pods — keep behavior, improve readability."
Agent: [reads pod_workers.go] [greps "newPodWorkers"] [edits file] [maybe runs tests]
Before any line changes, the agent has to answer a structural question: who depends on this symbol, transitively? The usual approaches all fall short at
| What the agent tries | What it gets | What it misses |
|---|---|---|
| Read one file | Local implementation | Callers in other packages |
grep newPodWorkers |
||
| Text matches | Upstream paths two or three hops away | |
| Rely on training data | Plausible Kubernetes architecture | This checkout's actual call graph |
| Read 20 caller files | More context, fewer tokens left for the edit | No guaranteed closure; easy to stop early |
How many times we have seen an agent confidently edit
a helper and skip the integration test package that actually exercises it. Blast radius closes that gap with a single subprocess (point rgctl -r
at your
local clone of kubernetes/kubernetes):
rgctl -r /path/to/kubernetes -f json blast-radius <Symbol>
The response is a few hundred tokens of structured JSON instead of tens of
thousands of lines of source the agent would otherwise have to read and still
fail to synthesize correctly. You can run the same command yourself in a
terminal — the point is not to replace your IDE, but to give the agent (and
you, when reviewing its plan) a shared, verifiable fact base.
rgctl is built around a loop that matches how Cursor, Claude Code, and similar
tools operate today:
1. Your prompt → natural language ("what breaks if I change X?")
2. Subprocess → rgctl -f json blast-radius <Symbol>
3. Structured facts → parse schema_version + payload
4. Reasoning → risk summary, test plan, edit scope
5. Edit / check → re-query if the graph may be stale
The contract lives in AGENTS.md
and installs as a project skill via rgctl install --skill
. Agents parse
stdout JSON only — not stderr, not the dashboard unless you ask for a UI.
When you review an agent's plan, ask whether it grounded impact analysis in
something like step 2, or whether it inferred callers from grep and memory.
That distinction is usually visible in the quality of the proposed test plan.
All numbers below come from indexing a fresh clone of
kubernetes/kubernetes — the same
tree you get from GitHub, not a fixture or subset. When working inside the
rgctl repo, ./scripts/fetch-profile-repos.sh
clones it to example/kubernetes
;
otherwise clone it anywhere and pass that path to -r
.
| Metric | Value |
|---|---|
| Source files indexed | 26,141 |
| Functions analyzed | 181,680 |
| Graph edges | 1,789,412 |
Functions named Run |
|
| 513 | |
Cold discover time |
|
| ~13 s | |
Typical blast-radius query |
|
| ~1 s |
No one should have to read 26k Go files to answer "what is the upstream impact of this symbol?".
IMHO it just doesnt make sense, whether a programmer or the helper agent.
Index once:
$ git clone https://github.com/kubernetes/kubernetes.git
$ rgctl -r kubernetes discover .
[✓] Loaded 26141 files -> 740986 nodes, 1789412 edges
[✓] Analyzed 181680 functions
[✓] Completed in 13.3s
discover
writes artifacts to kubernetes/.rgctl/
(or wherever you cloned),
including blast_engine.snapshot.bin
and a macro call lookup cache. Queries
are mmap lookups, not live graph walks over 1.7M edges.
RESTClientFor
Suppose you ask your agent to add a default timeout to RESTClientFor
in
client-go/rest/config.go
.
Without blast radius, a competent agent will open the file, grep for
RESTClientFor(
, find a handful of call sites, edit, and suggest:
go test ./staging/src/k8s.io/client-go/rest/...
That is not wrong. It is incomplete.
With blast radius, one query returns the full upstream picture:
$ rgctl -r kubernetes -f json blast-radius RESTClientFor
{
"schema_version": 2,
"target": {
"canonical_fqn": "RESTClientFor",
"file_path": "kubernetes/staging/src/k8s.io/client-go/rest/config.go",
"signature": "func RESTClientFor(config *Config) (*RESTClient, error) {"
},
"metrics": {
"direct_callers_count": 38,
"impact_zone_size": 95,
"score": 44.75
},
"topology": {
"direct_callers": [
{ "fqn": "factoryImpl.RESTClient" },
{ "fqn": "NewForConfig" },
{ "fqn": "Framework.BeforeEach" }
],
"impact_zone": [
{ "fqn": "NewKubectlCommand" },
{ "fqn": "main", "file_path": ".../cmd/kubectl/kubectl.go" }
]
}
}
From this you — and the agent — can derive a concrete plan:
cmd/kubectl/main
rest/
are necessary but not sufficient.You might reach the same conclusion with IDE find-references and ten minutes of
clicking. The agent will not do that reliably unless you give it a tool that
returns the closure in one shot.
This matters if you care about agent turn quality, not just correctness:
| Approach | Approx. context cost | Caller coverage |
|---|---|---|
Read config.go + five caller files |
||
| 15k–40k tokens | Partial | |
rgctl -f json blast-radius RESTClientFor |
||
| 0.5k–2k tokens | Full static closure |
The agent keeps context for the actual edit and for reasoning about risk, rather
than spending it reconstructing a call graph from grep output.
newPodWorkers
A subtler case. You ask the agent to extract pod worker configuration into a
struct in pkg/kubelet/pod_workers.go
.
Grep finds four direct call sites. That sounds low-risk.
$ rgctl -r kubernetes blast-radius newPodWorkers \
--file pkg/kubelet/pod_workers.go
Blast radius for 'newPodWorkers'
Score: 41.5/100
Direct callers: 4
Impact zone: 30
Callers: TestFakePodWorkers, createPodWorkersWithLogger, NewMainKubelet,
TestVolumeAttachLimitExceededCleanup
Impact: ... NewMainKubelet, createAndInitKubelet, RunKubelet, startKubelet,
Run, run, NewKubeletCommand, main
Four direct callers, but 30 in the impact zone — the chain runs through
kubelet construction all the way to cmd/kubelet/main
.
An agent that stops at grep will under-scope the test plan. One that reads blast
radius should propose pkg/kubelet
tests and the cmd/kubelet
startup path,
and mention RunKubelet
in the change summary.
You might get there by tracing callers in your IDE or by running
make test WHAT=pkg/kubelet
and seeing what breaks. Blast radius is how you
front-load that knowledge before the agent edits, not after CI fails.
--depth
to control noise Kubernetes impact zones include a lot of test harness symbols. When you want a
local picture rather than the full release surface:
$ rgctl -r kubernetes blast-radius newPodWorkers \
--file pkg/kubelet/pod_workers.go --depth 2
Impact zone: 8 # down from 30
Full closure for "what could this break in production?" Bounded depth for
"what is immediately upstream?" Same command, one flag.
You ask: "Kubelet.Run looks unused — can we remove it?"
$ rgctl -r kubernetes blast-radius Kubelet::Run \
--file pkg/kubelet/kubelet.go
Score: 0.0/100
Direct callers: 0
Impact zone: 0
A score of zero here does not mean safe to delete. Kubelet.Run
is the
kubelet's main loop; it is started from cmd/kubelet
. For this symbol the gap
is primarily interface dispatch — fast static analysis (without heavy
pointer analysis) struggles to link a caller that holds an interface type to the
concrete Kubelet
implementation. Goroutine spawns can produce the same blind
spot: the graph may not record them as CALLS
edges either.
You would not delete it. An agent might, if it treats the score as ground truth.
This is the most important caveat when relying on blast radius in agent
workflows: the output is a risk signal for reasoning, not permission to merge. When score is zero on a symbol you know is hot, disambiguate with
--class
or --file
, then grep for call sites the graph cannot see. TheWhether you or your agent runs the query, these are the fields worth paying
attention to:
| Field | Why it matters |
|---|---|
target.canonical_fqn |
|
Disambiguated symbol — not just a name that matches 513 Run functions |
|
target.file_path |
|
| Anchor for edits and citations | |
metrics.score |
|
| Quick risk tier: 0 / ~25–50 / 50+ | |
metrics.direct_callers_count |
|
| Immediate fan-in | |
metrics.impact_zone_size |
|
| Transitive fan-in | |
topology.direct_callers[] |
|
| First-hop checklist for tests and review | |
topology.impact_zone[] |
|
| Full upstream checklist | |
topology.scc_component_id |
|
| Hint when the symbol sits in a cycle-heavy neighborhood | |
gatekeeping.policy_status |
|
VIOLATED when a policy file rejects the change |
Text output is fine for a quick terminal check. Agent workflows and CI gates
should use -f json
per the JSON API.
Kubernetes has 513 functions named Run
. A bare symbol query fails loudly:
$ rgctl -r kubernetes blast-radius Run
Error: Symbol 'Run' is ambiguous. Found 513 matches.
Remediation: rgctl blast-radius "ClassName::Run"
rgctl blast-radius "path/to/file.go::Run"
You click the right reference in your IDE. An agent needs the remediation path
in the error output — and a rule that says never pick a random row from the
disambiguation table. In practice: retry with ClassName::symbol
, --file
, or
--class
before editing.
If you are setting up agent rules or reviewing agent output, check whether
disambiguation happened before the blast-radius numbers were quoted.
You can wire blast radius into automated guardrails:
{ "max_impact_nodes": 50 }
bash
$ rgctl -r kubernetes -f json blast-radius RESTClientFor \
--policy-file policy.json
For an agent proposing a large refactor, VIOLATED
should mean stop and report
— not silently proceed. The check
command runs the same rules across all
touched symbols in one pass, which fits agent-opened PRs where you want a hard
ceiling on impact zone size.
You might override that judgment on a case-by-case basis. Agents should not
override it unless you explicitly say so.
Blast radius is not the right first move for every task — even in agent-assisted
work:
| Your question | Often better |
|---|---|
| What calls this in the file I have open? | IDE find references |
| Will CI pass? | Run the tests |
| Is this a public API? | Module boundaries, docs, review |
| Who owns this package? | CODEOWNERS, team knowledge |
| What changed in this branch? | git diff |
Blast radius earns its place when you need transitive upstream impact as a bounded fact — before an agent edits a shared helper, before you approve its
SyncPod
may show zero callers; the
graph does not always resolve interface-typed receiversTest*
symbols; use
--depth
or path filters when you care about production paths onlytopology
, it was not
in the query result; treat that as a hallucination riskrgctl
from rgctl discover . --export-migration-hints
to index your application.
export REPO="$(pwd)/kubernetes"
rgctl -r "$REPO" discover .
rgctl -r "$REPO" -f json blast-radius RESTClientFor
rgctl -r "$REPO" -f json blast-radius newPodWorkers --file pkg/kubelet/pod_workers.go
If you are already in the rgctl repo, ./scripts/fetch-profile-repos.sh
clones
kubernetes/kubernetes into example/kubernetes
— use that path for -r
instead.
A useful sanity-check prompt for your agent:
Use rgctl to find the blast radius of RESTClientFor, and tell me what tests
we should run before changing it based on the upstream callers.
The explicit tool invocation matters — agents sometimes skip subprocesses unless
the prompt names them. A good answer runs rgctl -f json blast-radius
and cites
metrics
and topology
. A weak one greps the tree and guesses.