diff --git a/src/strands/telemetry/tracer.py b/src/strands/telemetry/tracer.py
index c03d9d962..997523988 100644
--- a/src/strands/telemetry/tracer.py
+++ b/src/strands/telemetry/tracer.py
@@ -693,16 +693,17 @@ def end_agent_span(
             if hasattr(response, "metrics") and hasattr(response.metrics, "accumulated_usage"):
                 if self.is_langfuse:
                     attributes.update({"langfuse.observation.type": "span"})
-                accumulated_usage = response.metrics.accumulated_usage
+                latest_invocation = response.metrics.latest_agent_invocation
+                usage = latest_invocation.usage if latest_invocation else response.metrics.accumulated_usage
                 attributes.update(
                     {
-                        "gen_ai.usage.prompt_tokens": accumulated_usage["inputTokens"],
-                        "gen_ai.usage.completion_tokens": accumulated_usage["outputTokens"],
-                        "gen_ai.usage.input_tokens": accumulated_usage["inputTokens"],
-                        "gen_ai.usage.output_tokens": accumulated_usage["outputTokens"],
-                        "gen_ai.usage.total_tokens": accumulated_usage["totalTokens"],
-                        "gen_ai.usage.cache_read_input_tokens": accumulated_usage.get("cacheReadInputTokens", 0),
-                        "gen_ai.usage.cache_write_input_tokens": accumulated_usage.get("cacheWriteInputTokens", 0),
+                        "gen_ai.usage.prompt_tokens": usage["inputTokens"],
+                        "gen_ai.usage.completion_tokens": usage["outputTokens"],
+                        "gen_ai.usage.input_tokens": usage["inputTokens"],
+                        "gen_ai.usage.output_tokens": usage["outputTokens"],
+                        "gen_ai.usage.total_tokens": usage["totalTokens"],
+                        "gen_ai.usage.cache_read_input_tokens": usage.get("cacheReadInputTokens", 0),
+                        "gen_ai.usage.cache_write_input_tokens": usage.get("cacheWriteInputTokens", 0),
                     }
                 )
 
diff --git a/tests/strands/telemetry/test_tracer.py b/tests/strands/telemetry/test_tracer.py
index 9176ce4ae..3d95d4b28 100644
--- a/tests/strands/telemetry/test_tracer.py
+++ b/tests/strands/telemetry/test_tracer.py
@@ -889,8 +889,11 @@ def test_end_agent_span(mock_span):
     tracer = Tracer()
 
     # Mock AgentResult with metrics
+    mock_invocation = mock.MagicMock()
+    mock_invocation.usage = {"inputTokens": 50, "outputTokens": 100, "totalTokens": 150}
     mock_metrics = mock.MagicMock()
-    mock_metrics.accumulated_usage = {"inputTokens": 50, "outputTokens": 100, "totalTokens": 150}
+    mock_metrics.accumulated_usage = {"inputTokens": 500, "outputTokens": 1000, "totalTokens": 1500}
+    mock_metrics.latest_agent_invocation = mock_invocation
 
     mock_response = mock.MagicMock()
     mock_response.metrics = mock_metrics
@@ -924,8 +927,11 @@ def test_end_agent_span_with_langfuse_observation_type(mock_span, monkeypatch):
     tracer = Tracer()
 
     # Mock AgentResult with metrics
+    mock_invocation = mock.MagicMock()
+    mock_invocation.usage = {"inputTokens": 50, "outputTokens": 100, "totalTokens": 150}
     mock_metrics = mock.MagicMock()
-    mock_metrics.accumulated_usage = {"inputTokens": 50, "outputTokens": 100, "totalTokens": 150}
+    mock_metrics.accumulated_usage = {"inputTokens": 500, "outputTokens": 1000, "totalTokens": 1500}
+    mock_metrics.latest_agent_invocation = mock_invocation
 
     mock_response = mock.MagicMock()
     mock_response.metrics = mock_metrics
@@ -960,8 +966,11 @@ def test_end_agent_span_latest_conventions(mock_span, monkeypatch):
     tracer = Tracer()
 
     # Mock AgentResult with metrics
+    mock_invocation = mock.MagicMock()
+    mock_invocation.usage = {"inputTokens": 50, "outputTokens": 100, "totalTokens": 150}
     mock_metrics = mock.MagicMock()
-    mock_metrics.accumulated_usage = {"inputTokens": 50, "outputTokens": 100, "totalTokens": 150}
+    mock_metrics.accumulated_usage = {"inputTokens": 500, "outputTokens": 1000, "totalTokens": 1500}
+    mock_metrics.latest_agent_invocation = mock_invocation
 
     mock_response = mock.MagicMock()
     mock_response.metrics = mock_metrics
@@ -999,6 +1008,56 @@ def test_end_agent_span_latest_conventions(mock_span, monkeypatch):
     mock_span.end.assert_called_once()
 
 
+def test_end_agent_span_uses_per_invocation_usage_not_accumulated(mock_span):
+    """Test that agent span reports per-invocation usage, not session-accumulated usage."""
+    tracer = Tracer()
+
+    # Simulate a multi-invocation session where accumulated_usage has grown large
+    # but the latest invocation only used a small amount of tokens
+    mock_invocation = mock.MagicMock()
+    mock_invocation.usage = {"inputTokens": 100, "outputTokens": 50, "totalTokens": 150}
+
+    mock_metrics = mock.MagicMock()
+    mock_metrics.accumulated_usage = {"inputTokens": 1000, "outputTokens": 500, "totalTokens": 1500}
+    mock_metrics.latest_agent_invocation = mock_invocation
+
+    mock_response = mock.MagicMock()
+    mock_response.metrics = mock_metrics
+    mock_response.stop_reason = "end_turn"
+    mock_response.__str__ = mock.MagicMock(return_value="Agent response")
+
+    tracer.end_agent_span(mock_span, mock_response)
+
+    call_args = mock_span.set_attributes.call_args[0][0]
+    # Should use per-invocation usage (100/50/150), NOT accumulated (1000/500/1500)
+    assert call_args["gen_ai.usage.input_tokens"] == 100
+    assert call_args["gen_ai.usage.output_tokens"] == 50
+    assert call_args["gen_ai.usage.total_tokens"] == 150
+    assert call_args["gen_ai.usage.prompt_tokens"] == 100
+    assert call_args["gen_ai.usage.completion_tokens"] == 50
+
+
+def test_end_agent_span_falls_back_to_accumulated_when_no_invocations(mock_span):
+    """Test fallback to accumulated_usage when no agent invocations exist."""
+    tracer = Tracer()
+
+    mock_metrics = mock.MagicMock()
+    mock_metrics.accumulated_usage = {"inputTokens": 200, "outputTokens": 100, "totalTokens": 300}
+    mock_metrics.latest_agent_invocation = None
+
+    mock_response = mock.MagicMock()
+    mock_response.metrics = mock_metrics
+    mock_response.stop_reason = "end_turn"
+    mock_response.__str__ = mock.MagicMock(return_value="Agent response")
+
+    tracer.end_agent_span(mock_span, mock_response)
+
+    call_args = mock_span.set_attributes.call_args[0][0]
+    assert call_args["gen_ai.usage.input_tokens"] == 200
+    assert call_args["gen_ai.usage.output_tokens"] == 100
+    assert call_args["gen_ai.usage.total_tokens"] == 300
+
+
 def test_end_model_invoke_span_with_cache_metrics(mock_span):
     """Test ending a model invoke span with cache metrics."""
     tracer = Tracer()
@@ -1035,14 +1094,23 @@ def test_end_agent_span_with_cache_metrics(mock_span):
     tracer = Tracer()
 
     # Mock AgentResult with metrics including cache tokens
-    mock_metrics = mock.MagicMock()
-    mock_metrics.accumulated_usage = {
+    mock_invocation = mock.MagicMock()
+    mock_invocation.usage = {
         "inputTokens": 50,
         "outputTokens": 100,
         "totalTokens": 150,
         "cacheReadInputTokens": 25,
         "cacheWriteInputTokens": 10,
     }
+    mock_metrics = mock.MagicMock()
+    mock_metrics.accumulated_usage = {
+        "inputTokens": 500,
+        "outputTokens": 1000,
+        "totalTokens": 1500,
+        "cacheReadInputTokens": 250,
+        "cacheWriteInputTokens": 100,
+    }
+    mock_metrics.latest_agent_invocation = mock_invocation
 
     mock_response = mock.MagicMock()
     mock_response.metrics = mock_metrics