When the agent breaks, which layer dropped the ball?
"The AI broke" is not an attribution. It is the absence of one — and it is how most agent postmortems still end. The model gets blamed, a prompt gets tweaked, and the same class of incident comes back a month later wearing a different stack trace.
This repository is the operator-facing failure-attribution dictionary for AI agent estates: a ten-way taxonomy that assigns every agent failure to the architecture layer that owns it, a postmortem template built around that attribution, and crosswalks that map the two major academic failure taxonomies — MAST and Microsoft Research's AgentRx — onto layers a production team can actually staff, budget, and fix.
The layers are the nine specifications of the Spine, the vendor-neutral architecture catalog for running AI agents in production. Five are public: PDS, ACS, ESF, AGS, DCS.
When an agent system misbehaves, the failure is one of ten kinds, and each kind has exactly one owning layer:
| # | The failure | Owned by | Layer | "It looked like…" |
|---|---|---|---|---|
| 1 | Bad tool data | PDS | Progressive Discovery Spine | Wrong supplier ID, stale internal cache, missing record, malformed tool call |
| 2 | Bad world data | ESF | External Signal Fabric | Expired signal, mis-tagged advisory, broken feed adapter |
| 3 | Bad reasoning | ACS (planner) | Adversarial Coordination Spine | Plan unsupported by the available signals |
| 4 | Bad evaluation | ACS (evaluator) | Adversarial Coordination Spine | Rubber-stamped contract violation, "verified" work that wasn't |
| 5 | Bad scoring | CRI | Composite Risk Index | One mystery number, no confidence band, no method declared |
| 6 | Bad governance | AGS | Agent Governance Spine | An action executed that policy should have denied — or a deny nobody can explain |
| 7 | Bad continuity | DCS | Durable Context Spine | Lost state, repeated work, a false "task complete" that survived into the next session |
| 8 | Bad grounding | GDS | Grounded Data Spine | Two agents give two answers to one metric; an agent saw data its user may not see |
| 9 | Bad or missing registry | ARS | Agent Registry Spine | A shadow agent nobody registered; a tool nobody knew was reachable |
| 10 | Bad or unbounded execution | SRS | Sovereign Runtime Spine | Runaway loop, unattributed action, an agent outliving its task |
The rule that makes the dictionary work: every failure gets exactly one primary owner. Secondary layers may share remediation, but a postmortem that ends with two owners has not finished attributing.
MAST (Cemri et al., "Why Do Multi-Agent LLM Systems Fail?") is the most-cited academic taxonomy of multi-agent failures: 14 failure modes in 3 categories, derived from 1,600+ annotated execution traces. It tells you what went wrong in the trace. This crosswalk adds who owns the fix.
Frequencies below are MAST's reported share of failures in its corpus.
| MAST mode | Freq. | Primary owner | Secondary | Why |
|---|---|---|---|---|
| FM-1.1 Disobey task specification | 11.8% | ACS | AGS | The negotiated contract between agents was violated and the evaluator let it through. If the disobeyed rule was a governance rule, the AGS gate should have made the action structurally impossible. |
| FM-1.2 Disobey role specification | 1.5% | ACS | — | Role separation (planner / generator / evaluator) is a structural property of the coordination layer, not a prompt aspiration. |
| FM-1.3 Step repetition | 15.7% | ACS | DCS | In-session loops are a coordination failure. Repetition across sessions means durable state failed to record what was already done. |
| FM-1.4 Loss of conversation history | 2.8% | DCS | — | The textbook continuity failure: state that should have survived, didn't. |
| FM-1.5 Unaware of termination conditions | 12.4% | ACS | DCS | Termination criteria belong in the coordination contract; the verification-gated completion ledger is the backstop. |
| FM-2.1 Conversation reset | 2.2% | DCS | — | An unexpected reset is a durable-state failure regardless of which agent triggered it. |
| FM-2.2 Fail to ask for clarification | 6.8% | ACS | — | Contract negotiation must surface missing information before execution; proceeding on guesswork is a coordination failure. |
| FM-2.3 Task derailment | 7.4% | ACS | — | Plan drift the evaluator should have caught. |
| FM-2.4 Information withholding | 0.85% | ACS | — | An inter-agent contract violation: the information existed and was not communicated. |
| FM-2.5 Ignored other agent's input | 1.9% | ACS | — | Same surface: coordination that discards signal. |
| FM-2.6 Reasoning–action mismatch | 13.2% | ACS | AGS | The action didn't follow from the stated reasoning. When the mismatched action touches the world, the deterministic gate is the last line. |
| FM-3.1 Premature termination | 6.2% | ACS | DCS | The evaluator accepted "done" too early; a verification-gated ledger refuses unverified completion by construction. |
| FM-3.2 No or incomplete verification | 8.2% | ACS | — | The checker was missing or structurally able to skip the check — the exact failure ACS's separated evaluator exists to prevent. |
| FM-3.3 Incorrect verification | 9.1% | ACS | — | The checker agreed with the maker. A checker that cannot disagree is not a checker. |
What the crosswalk shows: MAST's corpus maps almost entirely onto two of the nine layers — coordination (ACS) and continuity (DCS). That is not a weakness of MAST; it studied multi-agent research systems, and coordination is exactly their failure surface. It is a measurement of how much of a production estate the research benchmarks never touch.
AgentRx (Microsoft Research) classifies failed agent trajectories into 9 mutually exclusive failure categories, derived by grounded-theory analysis of 115 manually annotated failures across τ-bench, Flash, and Magentic-One.
| AgentRx category | Definition (verbatim) | Primary owner | Secondary | Why |
|---|---|---|---|---|
| Plan Adherence Failure | "Ignored required steps / did extra unplanned actions" | ACS | AGS | Contract violation first; if the extra action reached the world unchecked, the gate failed too. |
| Invention of New Information | "Altered facts not grounded in trace/tool output" | GDS | ESF | Grounding failure: the answer must be traceable to canonical, entitled data — or to a provenance-stamped external signal. |
| Invalid Invocation | "Tool call malformed / missing args / schema-invalid" | PDS | — | The tool surface owns its contract. Schema-invalid calls mean the discovery layer handed the agent a tool without an enforceable interface. |
| Misinterpretation of Tool Output | "Read tool output incorrectly; acted on wrong assumptions" | PDS | ACS | Ambiguous output is a tool-contract failure before it is a reasoning failure. Typed, disambiguated outputs shrink this class structurally. |
| Intent–Plan Misalignment | "Misread user goal/constraints and planned wrongly" | ACS | — | Planning discipline: the negotiated contract exists to pin the goal before execution. |
| Under-specified User Intent | "Could not proceed because required info wasn't available" | ACS | — | The coordination layer must surface the gap and ask — MAST's FM-2.2 is the same failure seen from the other side. |
| Intent Not Supported | "No available tool can do what's being asked" | PDS | ARS | Discovery should answer "there is no such tool" honestly — which requires a registry that actually knows what exists. |
| Guardrails Triggered | "Execution blocked by safety/access restrictions" | AGS | — | The gate fired. Attribution decides whether the deny was correct (working as designed) or the policy was wrong (governance debt) — either way, AGS owns the surface. |
| System Failure | "Connectivity/tool endpoint failures" | SRS | ESF | The execution substrate owns infrastructure failure — degraded modes, retries, bounded blast radius. A failing external feed is ESF's adapter surface. |
Stack the two crosswalks and count: the academic corpora concentrate on ACS, DCS, PDS, and touch GDS, AGS, SRS once each. Three layers receive no coverage at all:
- CRI (bad scoring) — research benchmarks don't ship mystery risk scores to executives.
- ARS (bad or missing registry) — a 115-trajectory benchmark has no shadow agents; a Fortune 500 running thousands of agents does.
- ESF (bad world data) — τ-bench doesn't make decisions on expired port-congestion signals. Supply chains do.
That is the honest reading of the literature: the taxonomies are excellent for what the benchmarks measure, and the benchmarks structurally cannot measure the failure surfaces that only exist at production scale — entitlements, registries, external signals, risk scores, runtimes. Those five surfaces are where "the AI broke" does the most financial damage, because nobody is even instrumented to name them.
POSTMORTEM-TEMPLATE.md is the operational half of this repository: a fill-in incident report whose core is the attribution walk — nine questions, asked in order, that end at exactly one owning layer. It includes the symptom classification (dictionary + MAST/AgentRx codes), the SLA impact line (which target from the owning spec's table was violated), and a prevention section that asks the only question that stops recurrence: what makes this failure structurally impossible now?
- Next incident, run the walk. Use the template as-is; it assumes nothing about your stack.
- Name the layer in the incident channel, not the model. "That's a DCS failure" changes who gets paged and what gets budgeted.
- Track attribution over quarters. The distribution tells you which spec to implement next — your estate's real gap, measured on your own incidents.
This dictionary is the meta-artifact of the Spine — nine versioned specifications for running AI agents in production, each owning one failure surface. Public specs: Progressive Discovery Spine · Adversarial Coordination Spine · External Signal Fabric · Agent Governance Spine · Durable Context Spine. CRI, GDS, ARS, and SRS are private; normative summaries available on request.
- Cemri et al., Why Do Multi-Agent LLM Systems Fail? — the MAST taxonomy: 3 failure categories, 14 failure modes, 1,600+ annotated traces. arXiv:2503.13657. Frequencies quoted from the paper.
- Microsoft Research, Systematic debugging for AI agents: introducing the AgentRx framework — 9 failure categories over the 115-trajectory AgentRx Benchmark (τ-bench, Flash, Magentic-One). Blog post. Category definitions quoted verbatim.
- The Spine specifications and per-layer SLA tables: saasquach.ai/spine.
- v1.0 (2026-07-16): initial public release — ten-way dictionary, MAST and AgentRx crosswalks, coverage analysis, postmortem template.
CC BY 4.0. Cite it, fork it, put it in your runbook — attribution appreciated. Authored by Drew Mattie, SaaSquach AI Labs (a division of Charles & Roe Inc.).