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Agents are tested across realistic scenarios, tools, memories, and control flows. Typical conditions for use: Agents act close to production; Tool and workflow boundaries must be verified. The central trade-off: Higher operational security is gained against expensive test data, mocks, and evaluation logic.
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Agent integration testing checks agents in realistic end-to-end scenarios across tools, memory states, and control flow so that observable behavior, not just single answers, can be evaluated reliably.
In agentic AI, the approach emerged from a practical problem: agents do not behave like static functions. Results depend on tools, memory, control flow, and model variants, and small prompt or model changes can introduce hidden behavioral regressions. The linked pattern catalog therefore frames repeatable end-to-end scenarios, fixtures, traces, and clear evaluation rules.
Think of a small production simulation in three steps: a scenario sets a reproducible starting state, the agent acts through its tools and decisions, and an evaluator checks the traces afterward. Instead of exact strings, the test asserts properties such as tool-call frequency, budget limits, allowed or rejected actions, and stable fallbacks. Runs can be seeded, repeated, and traced when needed.
Agents are treated as coordinated, specialized units, so tests must capture handoffs and boundaries between roles.
Fixed input data, state, and environment assumptions make complex flows reproducible.
The test checks observable properties such as frequency, limits, permission, or rejection instead of exact wording.
A model can complement rule-based checks with semantic quality or comparison judgments when simple rules are too coarse.
Spans across tool and service boundaries make the flow, root causes, and correlations visible during test runs.
This approach is useful when agents operate close to production, when tool or workflow boundaries must be verified, or when prompt and model changes may cause regressions. Its value increases with good test data, reproducible environments, and clear assertions; the trade-off is cost in fixtures, mocks, evaluation logic, and the maintenance of flaky cases. It is usually too heavy for isolated prompt checks.
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