Overview
Evaluating a single LLM response is hard. Evaluating an agent that takes sequences of actions, calls tools, maintains state across turns, and operates in open-ended environments is dramatically harder. Standard NLP benchmarks do not capture the compound failure modes unique to agentic systems.
Our evaluation research develops frameworks that assess agents holistically — from individual step correctness to end-to-end task success, from benign operation to adversarial stress testing — and that produce metrics operators can act on.
Evaluation Dimensions
Task Completion Rate
The primary outcome metric: does the agent successfully accomplish the stated goal within budget constraints (token, time, API calls)? Measured end-to-end across diverse task distributions.
Step-Level Accuracy
Fine-grained assessment of each individual action in the agent's trajectory — correct tool selection, valid parameter construction, and appropriate response interpretation.
Safety & Guardrail Adherence
Does the agent correctly refuse harmful requests, respect access boundaries, and invoke human-in-the-loop checkpoints when uncertainty is high? Tested with red-team prompts and adversarial scenarios.
Hallucination & Factuality
Agents citing external data must be evaluated for grounding fidelity — whether claims are supported by retrieved context and whether tool results are accurately interpreted.
Robustness & Adversarial Resilience
Performance under prompt injection, tool poisoning, distractor context, and distribution-shifted task inputs. Critical for production deployments in adversarial environments.
Efficiency & Cost
Token consumption, latency, and API cost per task completion. Agents that succeed but are prohibitively expensive are not production-ready.
Benchmark Landscape
We track and contribute to the evolving benchmark ecosystem for agentic evaluation, adapting public benchmarks for domain-specific enterprise contexts where general-purpose scores do not transfer.
- AgentBench — Multi-environment benchmark covering OS, database, knowledge graph, web browsing, and game tasks — a broad baseline for general agent capability.
- GAIA — Real-world question answering requiring multi-step tool use and reasoning. Strong proxy for enterprise information retrieval agent performance.
- SWE-bench — Software engineering tasks requiring agents to resolve GitHub issues by editing code. Relevant for DevSecOps and code-generation agent evaluation.
- Domain-Specific Suites — AppSofa builds proprietary evaluation suites for fraud detection, compliance checking, and federal intelligence tasks where public benchmarks have insufficient coverage.
AppSofa Evaluation Framework
Collaborate
Need to evaluate your agentic AI system?
We design and run evaluation programs for enterprise and federal agentic AI systems — from baseline benchmarking to red-team adversarial testing.
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