diff --git a/pyproject.toml b/pyproject.toml
index 605d5fe..0e3b285 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,9 @@ dependencies = [
     "chonkie>=1.7.0",
     "fastapi>=0.139.2",
     "fpdf2>=2.8.7",
+    "langchain>=1.3.14",
+    "langchain-openai>=1.3.5",
+    "langgraph>=1.2.9",
     "mcp>=1.28.1",
     "openai>=2.46.0",
     "paho-mqtt>=2.1.0",
diff --git a/src/torq/agent/diagnose.py b/src/torq/agent/diagnose.py
index c78a55e..f7ed10f 100644
--- a/src/torq/agent/diagnose.py
+++ b/src/torq/agent/diagnose.py
@@ -1,7 +1,11 @@
-"""Diagnosis agent: retrieve manual + history context, call the LLM, return a Diagnosis.
+"""Diagnosis agent: retrieve manual + history context, reason, return a Diagnosis.
+
+The agent runs a multi-step reason/act loop: it searches manuals, then repair
+history, and may search again with a refined query before answering. If the
+agent loop fails for any reason it falls back to a single-shot diagnosis.
 
 Retrieval goes through the MCP knowledge server (proving plant data stays
-on-premise).  If the MCP server is unreachable the agent falls back to
+on-premise).  If the MCP server is unreachable retrieval falls back to
 calling ``ingest.search`` directly.
 """
 
@@ -11,7 +15,12 @@
 
 from openai import OpenAI
 
-from torq.agent.prompts import SYSTEM, build_user_prompt
+from torq.agent.prompts import (
+    REACT_SYSTEM,
+    SYSTEM,
+    build_react_task,
+    build_user_prompt,
+)
 from torq.agent.schemas import Diagnosis
 from torq.config import settings
 from torq.ingest import search  # direct fallback
@@ -131,37 +140,70 @@ def _chat(client: OpenAI, messages: list[dict], json_mode: bool = True):
     return client.chat.completions.create(model=settings.llm_model, messages=messages, stream=False)
 
 
-# ── diagnosis cache ──────────────────────────────────────────────────────────
-
-# Recent diagnoses keyed by (machine, fault_code, context). A repeat fault with
-# the same context within the TTL reuses the cached result and skips the
-# retrieval + LLM round-trip. Context is part of the key so a recurring fault
-# described differently (new symptom text) re-diagnoses instead of reusing a
-# stale answer. No lock: a rare race just costs a redundant diagnosis, it never
-# corrupts state. Copies go in and out so a caller mutating a Diagnosis cannot
-# poison the cache.
-_CACHE: dict[tuple[str, str, str], tuple[float, Diagnosis]] = {}
-
-
-# ── main entry point ─────────────────────────────────────────────────────────
-
+def _merge_sources(data: dict, extra: list[str]) -> None:
+    """Add retrieved source ids to data['sources'] without duplicates."""
+    data.setdefault("sources", [])
+    for s in extra:
+        if s and s not in data["sources"]:
+            data["sources"].append(s)
 
-def diagnose(fault_code: str, machine: str = "", context: str = "") -> Diagnosis:
-    key = (machine, fault_code, context)
-    ttl = settings.diagnose_cache_ttl
-    if ttl > 0:
-        hit = _CACHE.get(key)
-        if hit and time.monotonic() < hit[0]:
-            log.info("Diagnosis cache hit for %s %s (reused, no LLM call)", machine, fault_code)
-            return hit[1].model_copy(deep=True)
 
+# ── multi-step (ReAct) agent ─────────────────────────────────────────────────
+
+
+def _diagnose_react(fault_code: str, machine: str, context: str) -> Diagnosis:
+    """Reason/act loop: the LLM decides when to search manuals/history vs answer."""
+    # Imported lazily so a missing/broken install degrades to one-shot.
+    from langchain.agents import create_agent
+    from langchain_core.tools import tool
+    from langchain_openai import ChatOpenAI
+
+    collected: list[str] = []  # source ids surfaced by tool calls this run
+    steps: list[str] = []  # ordered record of what the agent searched
+
+    @tool
+    def search_manuals(query: str) -> str:
+        """Search the plant's OEM manuals for excerpts relevant to a fault or symptom."""
+        steps.append(f"Searched manuals: {query}")
+        txt, src = _join_manuals(_fetch_manuals(query))
+        collected.extend(src)
+        return txt or "no manual excerpts found"
+
+    @tool
+    def search_history(query: str) -> str:
+        """Search past repair records for fixes to similar faults."""
+        steps.append(f"Searched repair history: {query}")
+        txt, src = _join_history(_fetch_history(query, machine=machine))
+        collected.extend(src)
+        return txt or "no past repairs found"
+
+    model = ChatOpenAI(
+        model=settings.agent_model,
+        api_key=settings.llm_api_key,
+        base_url=settings.llm_base_url,
+        temperature=0,
+    )
+    agent = create_agent(model, [search_manuals, search_history], system_prompt=REACT_SYSTEM)
+    result = agent.invoke(
+        {"messages": [("user", build_react_task(fault_code, machine, context))]},
+        {"recursion_limit": settings.agent_max_steps * 2},
+    )
+    data = _parse_json(result["messages"][-1].content)
+    _merge_sources(data, collected)
+    data["investigation"] = steps
+    return Diagnosis(fault_code=fault_code, machine=machine, **data)
+
+
+# ── single-shot fallback ─────────────────────────────────────────────────────
+
+
+def _diagnose_oneshot(fault_code: str, machine: str, context: str) -> Diagnosis:
+    """One retrieval pass then one LLM call. Used when the agent loop fails."""
     query = f"{fault_code} {machine} {context}".strip()
 
-    manuals_hits = _fetch_manuals(query)
-    manuals_txt, m_src = _join_manuals(manuals_hits)
-
-    history_hits = _fetch_history(query, machine=machine)
-    history_txt, h_src = _join_history(history_hits)
+    manuals_txt, m_src = _join_manuals(_fetch_manuals(query))
+    history_txt, h_src = _join_history(_fetch_history(query, machine=machine))
+    steps = [f"Searched manuals: {query}", f"Searched repair history: {query}"]
 
     client = OpenAI(api_key=settings.llm_api_key, base_url=settings.llm_base_url)
     messages = [
@@ -177,13 +219,41 @@ def diagnose(fault_code: str, machine: str = "", context: str = "") -> Diagnosis
         resp = _chat(client, messages)
         data = _parse_json(resp.choices[0].message.content)
 
-    # Prefer retrieved sources; let the model add any it names.
-    data.setdefault("sources", [])
-    for s in m_src + h_src:
-        if s not in data["sources"]:
-            data["sources"].append(s)
+    _merge_sources(data, m_src + h_src)
+    data["investigation"] = steps
+    return Diagnosis(fault_code=fault_code, machine=machine, **data)
+
+
+# ── diagnosis cache ──────────────────────────────────────────────────────────
+
+# Recent diagnoses keyed by (machine, fault_code, context). A repeat fault with
+# the same context within the TTL reuses the cached result and skips the whole
+# reason/act loop. Context is part of the key so a recurring fault described
+# differently (new symptom text) re-diagnoses instead of reusing a stale answer.
+# No lock: a rare race just costs a redundant diagnosis, it never corrupts state.
+# Copies go in and out so a caller mutating a Diagnosis cannot poison the cache.
+_CACHE: dict[tuple[str, str, str], tuple[float, Diagnosis]] = {}
+
+
+# ── main entry point ─────────────────────────────────────────────────────────
+
+
+def diagnose(fault_code: str, machine: str = "", context: str = "") -> Diagnosis:
+    """Cached multi-step (ReAct) diagnosis, falling back to single-shot on failure."""
+    key = (machine, fault_code, context)
+    ttl = settings.diagnose_cache_ttl
+    if ttl > 0:
+        hit = _CACHE.get(key)
+        if hit and time.monotonic() < hit[0]:
+            log.info("Diagnosis cache hit for %s %s (reused, no LLM call)", machine, fault_code)
+            return hit[1].model_copy(deep=True)
+
+    try:
+        diag = _diagnose_react(fault_code, machine, context)
+    except Exception:  # noqa: BLE001 - any agent failure degrades to one-shot
+        log.warning("ReAct diagnosis failed, falling back to single-shot", exc_info=True)
+        diag = _diagnose_oneshot(fault_code, machine, context)
 
-    diag = Diagnosis(fault_code=fault_code, machine=machine, **data)
     if ttl > 0:
         _CACHE[key] = (time.monotonic() + ttl, diag.model_copy(deep=True))
     return diag
diff --git a/src/torq/agent/prompts.py b/src/torq/agent/prompts.py
index 1fe649f..48e4a81 100644
--- a/src/torq/agent/prompts.py
+++ b/src/torq/agent/prompts.py
@@ -11,6 +11,30 @@
 )
 
 
+REACT_SYSTEM = (
+    "You are a maintenance diagnosis engine for industrial machines. Work in steps.\n"
+    "1. Call search_manuals ONCE with your best focused query about the fault.\n"
+    "2. Call search_history ONCE to find how similar faults were fixed before.\n"
+    "3. Reason about the root cause. Only search again if a result was empty or "
+    "clearly contradicts the fault, and only with a meaningfully different query. "
+    "Do not repeat similar searches.\n"
+    "Ground every claim in the retrieved context; do not invent part numbers.\n"
+    "When confident, STOP calling tools and reply with ONE JSON object only, no prose, "
+    "no markdown fences, matching:\n"
+    '{"root_cause": str, "confidence": float 0-1, "repair_steps": [str], '
+    '"parts": [str], "tools": [str], "safety_warnings": [str], "sources": [str]}'
+)
+
+
+def build_react_task(fault_code: str, machine: str, context: str) -> str:
+    return (
+        f"FAULT CODE: {fault_code}\n"
+        f"MACHINE: {machine or 'unknown'}\n"
+        f"SITUATION: {context or 'n/a'}\n\n"
+        "Diagnose this fault. Retrieve manuals and repair history first, then answer."
+    )
+
+
 def build_user_prompt(
     fault_code: str, machine: str, context: str, manuals: str, history: str
 ) -> str:
diff --git a/src/torq/agent/schemas.py b/src/torq/agent/schemas.py
index a08a6c2..160c314 100644
--- a/src/torq/agent/schemas.py
+++ b/src/torq/agent/schemas.py
@@ -19,6 +19,7 @@ class Diagnosis(BaseModel):
     tools: list[str] = []
     safety_warnings: list[str] = []
     sources: list[str] = []
+    investigation: list[str] = []  # ordered retrieval steps the agent took
 
 
 class WorkOrder(BaseModel):
@@ -32,6 +33,7 @@ class WorkOrder(BaseModel):
     safety_warnings: list[str] = []
     required_skill: str = "general"
     sources: list[str] = []  # manual sections + past repairs the diagnosis cited
+    investigation: list[str] = []  # ordered retrieval steps the agent took
     content: dict[str, str] = {}  # language code (fr/ar/en) -> formatted text
     status: str = "pending"  # pending | approved | rejected | dispatched | resolved | failed
     assigned_to: str | None = None
diff --git a/src/torq/config.py b/src/torq/config.py
index 441fb5b..b534e1d 100644
--- a/src/torq/config.py
+++ b/src/torq/config.py
@@ -17,6 +17,10 @@ class Settings(BaseSettings):
     llm_api_key: str = ""
     llm_base_url: str = "https://api.deepseek.com"
     llm_model: str = "deepseek-reasoner"
+    # Model for the multi-step (ReAct) agent. Needs tool-calling support, which
+    # the reasoner model lacks, so default to the chat model.
+    agent_model: str = "deepseek-chat"
+    agent_max_steps: int = 8  # reason/act ceiling; generous so the agent answers vs erroring into fallback
 
     # Vector DB (Qdrant)
     qdrant_url: str = ""
diff --git a/src/torq/workorder/generate.py b/src/torq/workorder/generate.py
index 14d9632..aec11a6 100644
--- a/src/torq/workorder/generate.py
+++ b/src/torq/workorder/generate.py
@@ -79,6 +79,7 @@ def build_work_order(
         safety_warnings=diag.safety_warnings,
         required_skill=_required_skill(diag.fault_code),
         sources=diag.sources,
+        investigation=diag.investigation,
         content=content,
         confidence=diag.confidence,
         fault_arrived_at=fault_arrived_at,
diff --git a/tests/test_diagnose_cache.py b/tests/test_diagnose_cache.py
index cd08fe5..e57c329 100644
--- a/tests/test_diagnose_cache.py
+++ b/tests/test_diagnose_cache.py
@@ -1,60 +1,55 @@
-"""Tests for the diagnosis cache: repeat faults skip the LLM within the TTL."""
+"""Tests for the diagnosis cache: repeat faults skip recomputation within the TTL."""
 
 import time
 import unittest
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 
 from torq.agent import diagnose as diag_mod
+from torq.agent.schemas import Diagnosis
 from torq.config import settings
 
 
-def _resp(content: str) -> MagicMock:
-    return MagicMock(choices=[MagicMock(message=MagicMock(content=content))])
-
-
-CANNED = '{"root_cause": "seized bearing", "repair_steps": ["swap bearing"]}'
+def _diag() -> Diagnosis:
+    # Fresh object per computation so cache copy-isolation is exercised.
+    return Diagnosis(fault_code="E-201", root_cause="seized bearing", repair_steps=["swap bearing"])
 
 
 class DiagnoseCacheTests(unittest.TestCase):
     def setUp(self) -> None:
         diag_mod._CACHE.clear()
-        # Patch retrieval + LLM so no vector DB or network is touched.
-        self._patchers = {
-            "man": patch("torq.agent.diagnose._fetch_manuals", return_value=[]),
-            "hist": patch("torq.agent.diagnose._fetch_history", return_value=[]),
-            "oai": patch("torq.agent.diagnose.OpenAI", return_value=MagicMock()),
-            "chat": patch("torq.agent.diagnose._chat", return_value=_resp(CANNED)),
-        }
-        started = {name: p.start() for name, p in self._patchers.items()}
-        self.mock_chat = started["chat"]
+        # Mock the whole diagnosis computation (ReAct path) so the cache is tested
+        # in isolation with no vector DB or network. call_count == cache misses.
+        self._patcher = patch(
+            "torq.agent.diagnose._diagnose_react", side_effect=lambda *a, **k: _diag()
+        )
+        self.mock_diag = self._patcher.start()
 
     def tearDown(self) -> None:
-        for p in self._patchers.values():
-            p.stop()
+        self._patcher.stop()
         diag_mod._CACHE.clear()
 
     def test_repeat_fault_served_from_cache(self) -> None:
         d1 = diag_mod.diagnose("E-201", "CM-350")
         d2 = diag_mod.diagnose("E-201", "CM-350")
-        self.assertEqual(self.mock_chat.call_count, 1)  # second call reused
+        self.assertEqual(self.mock_diag.call_count, 1)  # second call reused
         self.assertEqual(d1.root_cause, d2.root_cause)
 
     def test_distinct_keys_not_shared(self) -> None:
         diag_mod.diagnose("E-201", "CM-350")
         diag_mod.diagnose("J-108", "CM-350")  # different fault_code
         diag_mod.diagnose("E-201", "PK-9")  # different machine
-        self.assertEqual(self.mock_chat.call_count, 3)
+        self.assertEqual(self.mock_diag.call_count, 3)
 
     def test_ttl_zero_disables_cache(self) -> None:
         with patch.object(settings, "diagnose_cache_ttl", 0):
             diag_mod.diagnose("E-201", "CM-350")
             diag_mod.diagnose("E-201", "CM-350")
-        self.assertEqual(self.mock_chat.call_count, 2)
+        self.assertEqual(self.mock_diag.call_count, 2)
 
     def test_different_context_not_shared(self) -> None:
         diag_mod.diagnose("E-471", "CM-350", "overtemp after 6h runtime")
         diag_mod.diagnose("E-471", "CM-350", "recurring overtemp, lint buildup")
-        self.assertEqual(self.mock_chat.call_count, 2)  # context busts the cache
+        self.assertEqual(self.mock_diag.call_count, 2)  # context busts the cache
 
     def test_expired_entry_recomputes(self) -> None:
         diag_mod.diagnose("E-201", "CM-350")
@@ -63,7 +58,7 @@ def test_expired_entry_recomputes(self) -> None:
         _exp, diag = diag_mod._CACHE[key]
         diag_mod._CACHE[key] = (time.monotonic() - 1, diag)
         diag_mod.diagnose("E-201", "CM-350")
-        self.assertEqual(self.mock_chat.call_count, 2)
+        self.assertEqual(self.mock_diag.call_count, 2)
 
     def test_cache_copy_isolation(self) -> None:
         d1 = diag_mod.diagnose("E-201", "CM-350")
diff --git a/tests/test_react_diagnosis.py b/tests/test_react_diagnosis.py
new file mode 100644
index 0000000..781d2b3
--- /dev/null
+++ b/tests/test_react_diagnosis.py
@@ -0,0 +1,104 @@
+"""Tests for the multi-step (ReAct) diagnosis agent and its single-shot fallback."""
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock, patch
+
+from torq.agent.diagnose import (
+    _diagnose_oneshot,
+    _diagnose_react,
+    _merge_sources,
+    diagnose,
+)
+from torq.agent.schemas import Diagnosis
+
+
+def _msg(content: str) -> SimpleNamespace:
+    return SimpleNamespace(content=content)
+
+
+class TestMergeSources:
+    def test_dedupes_and_skips_empty(self):
+        data = {"sources": ["A"]}
+        _merge_sources(data, ["A", "B", "", None, "B"])
+        assert data["sources"] == ["A", "B"]
+
+    def test_creates_field_when_missing(self):
+        data: dict = {}
+        _merge_sources(data, ["X"])
+        assert data["sources"] == ["X"]
+
+
+class TestDiagnoseReact:
+    @patch("langchain_openai.ChatOpenAI")
+    @patch("langchain.agents.create_agent")
+    def test_returns_diagnosis_with_injected_ids(self, mock_create, _mock_chat):
+        """Final agent message is parsed; fault_code/machine come from the caller."""
+        agent = MagicMock()
+        agent.invoke.return_value = {
+            "messages": [
+                _msg('{"root_cause": "worn seal", "confidence": 0.8, "sources": ["REP-1"]}')
+            ]
+        }
+        mock_create.return_value = agent
+
+        result = _diagnose_react("E-201", "CNC-LatheA", "")
+
+        assert isinstance(result, Diagnosis)
+        assert result.root_cause == "worn seal"
+        assert result.fault_code == "E-201"
+        assert result.machine == "CNC-LatheA"
+        assert "REP-1" in result.sources
+
+    @patch("langchain_openai.ChatOpenAI")
+    @patch("langchain.agents.create_agent")
+    def test_parses_fenced_json(self, mock_create, _mock_chat):
+        agent = MagicMock()
+        agent.invoke.return_value = {
+            "messages": [_msg('```json\n{"root_cause": "cavitation"}\n```')]
+        }
+        mock_create.return_value = agent
+
+        result = _diagnose_react("P-260", "Pump 3", "")
+
+        assert result.root_cause == "cavitation"
+
+
+class TestFallback:
+    @patch("torq.agent.diagnose._diagnose_oneshot")
+    @patch("torq.agent.diagnose._diagnose_react")
+    def test_react_failure_falls_back(self, mock_react, mock_oneshot):
+        mock_react.side_effect = RuntimeError("model has no tool support")
+        sentinel = Diagnosis(fault_code="E-201", root_cause="fallback")
+        mock_oneshot.return_value = sentinel
+
+        result = diagnose("E-201", "CNC-LatheA")
+
+        mock_react.assert_called_once()
+        mock_oneshot.assert_called_once_with("E-201", "CNC-LatheA", "")
+        assert result is sentinel
+
+    @patch("torq.agent.diagnose._diagnose_react")
+    def test_react_success_skips_fallback(self, mock_react):
+        sentinel = Diagnosis(fault_code="E-201", root_cause="react")
+        mock_react.return_value = sentinel
+
+        assert diagnose("E-201") is sentinel
+
+
+class TestOneshot:
+    @patch("torq.agent.diagnose.OpenAI")  # avoid needing real credentials on CI
+    @patch("torq.agent.diagnose._fetch_history", return_value=[])
+    @patch("torq.agent.diagnose._fetch_manuals", return_value=[])
+    @patch("torq.agent.diagnose._chat")
+    def test_parses_and_returns_diagnosis(self, mock_chat, _m, _h, _client):
+        mock_chat.return_value = SimpleNamespace(
+            choices=[SimpleNamespace(message=_msg('{"root_cause": "worn seal"}'))]
+        )
+
+        result = _diagnose_oneshot("E-201", "CNC-LatheA", "")
+
+        assert result.root_cause == "worn seal"
+        assert result.fault_code == "E-201"
+        assert len(result.investigation) == 2  # manuals + history steps recorded
diff --git a/uv.lock b/uv.lock
index 6959b31..9817848 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1040,6 +1040,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/f7/18a1afcd64f35314b68c1f23afcd9994d0bc13e65cc77517afff4e83986d/jiter-0.16.0-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64d613743df53199b1aa256a7d328340da6d7078aac7705a7db9d7a791e9cfd2", size = 343885, upload-time = "2026-06-29T13:05:12.087Z" },
 ]
 
+[[package]]
+name = "jsonpatch"
+version = "1.33"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jsonpointer" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/42/78/18813351fe5d63acad16aec57f94ec2b70a09e53ca98145589e185423873/jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c", size = 21699, upload-time = "2023-06-26T12:07:29.144Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/73/07/02e16ed01e04a374e644b575638ec7987ae846d25ad97bcc9945a3ee4b0e/jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade", size = 12898, upload-time = "2023-06-16T21:01:28.466Z" },
+]
+
+[[package]]
+name = "jsonpointer"
+version = "3.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/18/c7/af399a2e7a67fd18d63c40c5e62d3af4e67b836a2107468b6a5ea24c4304/jsonpointer-3.1.1.tar.gz", hash = "sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900", size = 9068, upload-time = "2026-03-23T22:32:32.458Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/6a/a83720e953b1682d2d109d3c2dbb0bc9bf28cc1cbc205be4ef4be5da709d/jsonpointer-3.1.1-py3-none-any.whl", hash = "sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca", size = 7659, upload-time = "2026-03-23T22:32:31.568Z" },
+]
+
 [[package]]
 name = "jsonschema"
 version = "4.26.0"
@@ -1067,6 +1088,150 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
 ]
 
+[[package]]
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     { name = "fastapi" },
     { name = "fpdf2" },
+    { name = "langchain" },
+    { name = "langchain-openai" },
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diff --git a/web/src/pages/Dashboard.jsx b/web/src/pages/Dashboard.jsx
index 5da6e5c..d5bc5f7 100644
--- a/web/src/pages/Dashboard.jsx
+++ b/web/src/pages/Dashboard.jsx
@@ -308,6 +308,15 @@ function Drawer({ workOrder, onClose }) {
           </div>
         )}
 
+        {w.investigation?.length > 0 && (
+          <details className={styles.investigation}>
+            <summary>Agent investigation ({w.investigation.length} steps)</summary>
+            <ol>
+              {w.investigation.map((s, i) => <li key={i}>{s}</li>)}
+            </ol>
+          </details>
+        )}
+
         {["en", "fr", "ar"].map((lng) =>
           w.content?.[lng] ? (
             <div key={lng} className={styles.langBlock}>
diff --git a/web/src/pages/Dashboard.module.css b/web/src/pages/Dashboard.module.css
index c06ab63..b2d87d2 100644
--- a/web/src/pages/Dashboard.module.css
+++ b/web/src/pages/Dashboard.module.css
@@ -565,6 +565,36 @@
   transition: color 0.35s;
 }
 
+.investigation {
+  margin: 20px 0;
+}
+
+.investigation summary {
+  color: var(--text-muted);
+  font-family: "ALTGumbo", system-ui, sans-serif;
+  font-weight: 700;
+  font-size: 11px;
+  text-transform: uppercase;
+  letter-spacing: 0.06em;
+  cursor: pointer;
+  transition: color 0.35s;
+}
+
+.investigation ol {
+  margin: 8px 0 0;
+  padding-left: 20px;
+}
+
+.investigation li {
+  color: var(--text-muted);
+  font-family: "ALTGumbo", system-ui, sans-serif;
+  font-weight: 400;
+  font-size: 13px;
+  line-height: 1.6;
+  margin: 4px 0;
+  transition: color 0.35s;
+}
+
 .langBlock {
   margin: 20px 0;
 }