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Browser Use

An AI agent capability where the model controls a real web browser—clicking, typing, navigating, and reading rendered pages—to automate workflows on websites that don't expose APIs. Browser-use agents operate vendor portals, legacy admin panels, government forms, and multi-tab research workflows by understanding pages semantically rather than relying on brittle CSS selectors.

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 9, 2026

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Computer Use

An AI agent capability where the model directly controls a desktop or browser—clicking buttons, typing text, navigating menus, and reading screens like a human user. Computer use agents can operate any software, even without an API, by interacting with the GUI. This enables automation of legacy systems, complex SaaS workflows, and tasks that span multiple applications without custom integrations.

Agent Observability

Monitoring and tracing every step an AI agent takes—LLM calls, tool invocations, decisions, and outputs—to debug failures, measure performance, and ensure reliability. Observability platforms (LangSmith, Arize, Braintrust) log agent traces, track latency and cost per step, surface error patterns, and alert on quality regressions. Essential for production agents where you need to understand why an agent took a specific action.

Agent Benchmarking

Standardized evaluation of AI agent performance across defined tasks, metrics, and baselines—enabling apples-to-apples comparison between different agent solutions. Benchmarks measure task completion rate, accuracy, latency, cost per task, and safety compliance on representative workloads. Examples include SWE-bench for coding agents and customer support benchmarks that test resolution accuracy across ticket categories.

AI Agent Testing

The systematic evaluation of AI agent behavior across functional, safety, and performance dimensions before and after deployment. Unlike traditional software testing (deterministic: same input produces same output), agent testing is probabilistic—the same input may produce different valid outputs. Testing approaches include golden dataset evaluation (test cases with known-correct answers), adversarial testing (attempts to break guardrails), regression testing (ensuring updates don't degrade behavior), and A/B testing (comparing agent versions on live traffic).

Agent Sandbox

An isolated execution environment where AI agents run with restricted permissions, preventing them from affecting production systems, accessing sensitive data, or taking irreversible actions during testing or untrusted execution. Sandboxes provide file system isolation, network restrictions, resource limits, and action logging—letting teams evaluate agent behavior safely before granting production access. The concept extends from traditional software sandboxing but adds AI-specific concerns like prompt injection resistance and tool-call validation.

Agent Trace

A structured log of every step an AI agent took during a single run—each LLM call, tool invocation, intermediate decision, token count, latency, and outcome. Traces are the primary debugging artifact for production agents: when a customer reports a bad outcome, the trace shows exactly which retrieval missed, which tool returned the wrong data, or which model decision led the run astray. Observability platforms (LangSmith, Braintrust, Arize, Logfire) specialize in capturing and querying agent traces. A trace is the debugging view of a run; when the same reasoning-and-action record is retained durably and made tamper-evident, it becomes the audit trail that underpins accountability — the trace answers 'why did it do that?', the audit trail proves it later.

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