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Adapters & Tools — Plain-English Overview

A quick guide to how adapters and tools work in AOBench.

Tools

What they are: Mock HPC services that agents call during a run. They simulate scheduler queries, docs lookups, telemetry, and RBAC checks.

What Description
Purpose Answer tool calls from the agent by reading data from the environment snapshot
Data source benchmark/environments/<env_id>/ — JSON, CSV, YAML files
Role awareness Behavior changes by role (e.g. scientific_user sees only their own jobs)
Examples slurm__query_jobs, slurm__job_details, docs__retrieve, telemetry__query_memory_events, rbac__check, facility__query_node_power, facility__query_rack_telemetry

Flow: Agent calls a tool → tool reads env data → returns a result (or permission denied).

Adapters

What they are: Bridges to different agent backends (OpenAI, Azure, a stub, etc.). They orchestrate the task loop and produce a trace.

What Description
Purpose Run the agent on a task; mediate between the agent and tools until an answer is produced
Input ExecutionContext — task, environment, tools
Output A Trace — steps, tool calls, observations, final answer
Examples direct_qa — stub that returns a placeholder with no tools; openai — OpenAI/Azure API with function calling

Flow: Receive task + context → send to agent → when agent wants tools, call tools and feed results back → repeat until agent stops with a final answer → return trace.

Connect-to-agent mode (planned post-v0.1): New adapters will invoke external HPC agents (ODA, ExaSage, …) deployed on or near clusters via HTTP / MCP / FastAPI. AOBench connects to the agent's API and scores its trace; AOBench never needs direct access to real SLURM or the cluster.

How They Work Together

┌─────────────────────────────────────────────────────────────┐
│  Runner                                                     │
│  1. Loads task + environment (deterministic snapshot)       │
│  2. Builds ToolRegistry — methods filtered by role          │
│  3. Passes task, tools → Adapter; receives Trace back       │
│  4. Passes Trace + task → Scorers; returns BenchmarkResult  │
└──────────────────────────────┬──────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────┐
│  Adapter  (e.g. OpenAIAdapter)                              │
│  • Sends task.query_text + tool schemas → LLM               │
│  • When LLM calls a tool → ToolRegistry.dispatch()   ─────┐ │
│  • Appends [tool call + observation] as a TraceStep  ◄────┘ │
│  • Repeats until LLM gives a final answer  (≤ 10 rounds)    │
│  • Returns completed Trace                                  │
└──────────────────────────────┬──────────────────────────────┘
                    dispatch   │
                               ▼
┌─────────────────────────────────────────────────────────────┐
│  Tools  (slurm · docs · telemetry · rbac · facility)        │
│  • Run in-process — no external APIs                        │
│  • Read from env bundle (deterministic snapshot)            │
│  • Wrong role → permission_denied observation               │
│  • Return JSON observation → Adapter                        │
└──────────────────────────────┬──────────────────────────────┘
                               │
                               │  ← Adapter returns completed Trace
                               ▼
┌─────────────────────────────────────────────────────────────┐
│  Scorers  (invoked by Runner · AggregateScorer)             │
│  • OutcomeScorer     — final answer vs gold                 │
│  • ToolUseScorer     — tool selection & argument quality    │
│  • GovernanceScorer  — RBAC compliance, permission checks   │
│  • GroundingScorer   — answer grounded in observations      │
│  • EfficiencyScorer  — step count  (≤ 5 steps → 1.0)        │
│                                                             │
│  → BenchmarkResult: aggregate_score (0–1) + DimensionScores │
└─────────────────────────────────────────────────────────────┘

Tool Reference

Tool Methods Data source Notes
slurm query_jobs, job_details, list_nodes, list_partitions slurm/slurm_state.json, slurm/job_details.json Role-aware: scientific_user sees own jobs only
docs retrieve docs/*.md Keyword search over documentation files
telemetry query_memory_events, list_metrics telemetry/*.csv Memory event time series
rbac check policy/*.yaml Permission checks by role + resource + action
facility query_node_power, query_cluster_energy, query_rack_telemetry, list_inventory power/*.csv, rack/*.csv, inventory/*.csv For facility_admin role; ENERGY tasks

Scorers Reference

Scorer Dimension What it measures
OutcomeScorer outcome Quality of final answer vs gold (exact / semantic / numeric match)
ToolUseScorer tool_use Tool selection coverage, precision, no redundancy
GroundingScorer grounding Fraction of answer's key claims supported by tool observations
GovernanceScorer governance RBAC compliance — penalises permission violations
EfficiencyScorer efficiency Step count efficiency (≤5 steps = 1.0, ≥20 = 0.0)

Scoring Profiles

Profile Use when grounding weight
alpha0_minimal Tasks with no tool expectation / stubs 0.00
alpha1_grounding Tasks where tool evidence is expected 0.20
default_hpc_v01 Full production benchmark 0.15

One-Line Summary

Component Role
Tools Simulate HPC APIs — read env data, enforce permissions
Adapters Connect to agent backends — drive the task loop, use tools when the agent requests them
Scorers Evaluate the trace on 7 dimensions and aggregate into one score