Agent Failure Registry
A neutral namespace for AI agent failure modes, and a crosswalk between the taxonomies that already exist.
Not another classifier. There are at least seven of those, each with its own
private vocabulary: Agent-Xray's tool_selection_bug, AgentRx's
Invalid Invocation, and five more describing overlapping reality under names
that share nothing. This is the mapping layer between them.
>>> import afr
>>> afr.map("agentrx", "Invalid Invocation")
[AF-0023 (exact)]
>>> afr.map("agent-xray", "spin")
[AF-0058 (exact)]
>>> afr.mode("AF-0058").layer
'harness'
The analogy is CVE, not CWE. Plenty of people have proposed taxonomies; nobody has done the boring institutional work of assigning stable ids, accepting mappings, and letting tools reference them. CVE's value was never intellectual — it was giving the whole industry a shared noun.
Why this and not a classifier
- It gets stronger as rivals ship. Every new classifier with a private taxonomy increases the need for the crosswalk. A classifier is threatened by the next classifier; a namespace is fed by it.
- Incumbents structurally cannot build it. A registry operated by one vendor is one every competitor is right to ignore. Microsoft declined the standards position for AgentRx explicitly.
- Distribution runs through other repos. You do not need an audience — you open a PR adding a mapping file to each classifier.
Install
pip install agent-failure-registry # zero runtime dependencies; imports as `afr`
Use
import afr
# One tool's report, tagged with shared ids
labels = afr.normalize({"stuck_loop": 31, "context_overflow": 18}, "agent-xray")
print(afr.profile(labels).render())
failure profile (source: agent-xray, n=49)
--------------------------------------------------------------------
AF-0058 63.3% ###################### Unproductive repetition
AF-0064 36.7% ############ Context overflow truncation
--------------------------------------------------------------------
by layer: harness=49
That is the pitch in one output. 67% pass tells you nothing you can act on.
afr modes # every mode
afr show AF-0142 # full record, plus who maps to it
afr map agentrx "System Failure" # source category -> AF ids
afr unmap AF-0064 agent-xray # and back, with relations inverted
afr coverage # how much of each taxonomy is mapped
afr gaps # what nobody has named yet
Two levels of identifier
AF-0142 |
mode | Permanent, versionless, like CWE. What classifiers map onto and humans cite. |
AFI-2026-08-0417 |
instance | One observation, pinned to model + harness version, like CVE. |
Conflating them breaks the query that justifies the whole project: did Opus 5.2 regress on AF-0142 relative to 5.1? That needs modes stable across time and instances pinned to versions — so they cannot be the same identifier.
Instances are deliberately not implemented yet. Open collection only once three independent tools emit AF ids.
Relation direction
relation describes the AF mode relative to the source category: exact,
broader, narrower, overlaps. afr.unmap() inverts it for you — swapping
broader and narrower — because hand-authoring the reverse is how crosswalks
silently corrupt.
At most one exact per source category. One wrong exact discredits the table.
Status
Pre-v0. 24 modes, all provisional; 6 source taxonomies, every one
mapped from a full read of its operative definitions (classifier code, judge
prompts, paper text, ground-truth annotations), with the evidence in
registry/crosswalk/notes/:
| source | kind | mapped |
|---|---|---|
| AgentRx | LLM judge, 10 labels | 9/10 |
| Agent-Xray | rule classifier, 22 labels | 19/22 |
| ToolFailBench | benchmark, 5 labels | 4/5 |
| AgentFail | 307 annotated failures, 16 root causes | 15/16 |
| Model or Harness? | 41 modes with fault side | 27/41 |
| Characterizing Faults | 385 framework defects, 14 categories | 5/14 — mostly out of scope by design |
No mode is an orphan: every AF id has at least one source, and the five
newest (AF-0153 required step omitted, AF-0157 wrong tool selected,
AF-0161 fabricated content, AF-0166 unnecessary action, AF-0170
external service failure) each have two or more. The rule that produced them:
a gap becomes a mode only when a second independent taxonomy names the same
thing — one mode per vendor category is how a shared namespace becomes a
house style.
afr gaps lists what is still unnamed. The ones with more than one source
behind them are the next candidates; the single-source ones (the whole memory
family, recovery failure, sycophancy, prompt injection, external access
blocks) wait for a second.
One open question the read surfaced rather than settled: Model or
Harness? puts repetition, delegation and
insufficient-sourcing failures on the model side because the information
to avoid them was in context; AFR currently has them on harness. See
notes/model-or-harness.md.
Repo layout
registry/modes/AF-####.yaml canonical mode records (CC0)
registry/crosswalk/*.yaml one file per third-party taxonomy (CC0)
registry/crosswalk/notes/ the full-read evidence behind each crosswalk
registry/SCHEMA.md field semantics and relation direction
afr/ the library (Apache-2.0), no dependencies
afr/_index.json compiled from YAML, committed, CI-enforced
scripts/compile.py YAML -> index (the only place PyYAML is needed)
scripts/validate.py integrity checks; a lying crosswalk is worse than none
Development
make check # build + validate + test
make demo # coverage, gaps, and a sample profile
Licensing
Registry data is CC0; the library is Apache-2.0. A namespace with an attribution clause invites legal review, and legal review is the friction that stops tools embedding it.
Governance, including the commitment to move to a neutral foundation once three independent tools map to this namespace, is in GOVERNANCE.md.
Outreach
Distribution runs through other projects' repos, not through this one. Drafted
mapping-file PRs for AgentRx and Agent-Xray, and an issue for AgentDebugX, are
in outreach/ — including the order to send them in, which
matters more than the speed.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file agent_failure_registry-0.2.0.tar.gz.
File metadata
- Download URL: agent_failure_registry-0.2.0.tar.gz
- Upload date:
- Size: 35.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c30aae4f8b60ebd4a3ef4c9ea3ba3d7f0f9cc1ee93280515927db32184781300
|
|
| MD5 |
d07cfe43a48fb529cb1ffd36fba83d15
|
|
| BLAKE2b-256 |
1d9ca38102f9fea2ef2e3fa9449bcd3bbb6b4501238f84a5f35638d20c7aa879
|
File details
Details for the file agent_failure_registry-0.2.0-py3-none-any.whl.
File metadata
- Download URL: agent_failure_registry-0.2.0-py3-none-any.whl
- Upload date:
- Size: 32.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e911dbe37a35163a057e787c1513f1b59770c1256d3fdffb6a74b257894baa1e
|
|
| MD5 |
3d3d4fce4b00e1f33d18ec4e434af5f2
|
|
| BLAKE2b-256 |
e862af6cb6311850f5b5e6960dff62b0bb9968bffffbbc80c7d55c09febc8c2f
|