diff --git a/docs/architecture/reproducibility/graphcertification.rst b/docs/architecture/reproducibility/graphcertification.rst
new file mode 100644
index 000000000..a413a3748
--- /dev/null
+++ b/docs/architecture/reproducibility/graphcertification.rst
@@ -0,0 +1,49 @@
+.. _reproducibility_graphcertification:
+
+Graph Certification
+===================
+'Certifying' a graph involves generating and publishing reproducibility signatures.
+These signatures can be integrated into a CI/CD pipeline, used during executions for verification or
+during late-stage development when fine-tuning graphs.
+
+By producing and sharing these signatures, subsequent changes to execution environment, processing
+components, overall graph design and data artefacts can be easily and efficiently tested.
+
+Certifying a Graph
+------------------
+The process of generating and storing workflow signatures is relatively straightforward.
+
+* From the root of the graph-storing directory (usually a repository) create a ``/reprodata/[GRAPH_NAME]`` directory.
+* Run the graph with the ``ALL`` reproducibility flag, and move the produced reprodata.out file to the previously created directory.
+* (optional) Run from ``dlg.common.reproducibility.reprodata_compare.py`` script with this file as input to generate a summary-csv file
+
+In subsequent executions or during CI/CD scripts:
+* Note the reprodata.out file generated during the test execution
+* Run ``dlg.common.reproduciblity.reprodata_compare.py`` with the published ``reprodata/[GRAPH_NAME]`` directory and newly generated signature file
+* The resulting ``[SESSION_NAME]-comparison.csv`` will contain a simple True/False summary for each RMode, for use at your discretion.
+
+What is to be expected?
+***********************
+In general, all but ``Recomputation`` and ``Replicate_Computational`` rmodes should match, moreover:
+
+* A failed ``Rerun`` indicates some fundamental structure is different
+* A failed ``Repeat`` indicates changes to component parameters or a different execution scale
+* A failed ``Recomputation~`` indicates some runtime environment changes have been made
+* A failed ``Reproduction`` indicates data artefacts have changed
+* A failed ``Scientific Replication`` indicates a change in data artefacts or fundamental structure
+* A failed ``Computational Replication`` indicates a change in data artefacts or runtime environment
+* A failed ``Total Replica`` indicates a change in data artefacts, component parameters or different execution scale
+
+When attempting to re-create some known graph-derived result, ``Replication`` is the goal.
+In an operational context, where data changes constantly, ``Reruning`` is the goal
+When conducting science across multiple trials, ``Repeating`` is necessary to use the derived data arte-facts in concert.
+
+Tips on Making Graphs Robust
+----------------------------
+The most common 'brittle' aspect of graphs are hard-coded paths to data resources and access to referenced data.
+This can be ameliorated by:
+
+* Using the ``$DLG_ROOT`` keyword in component parameters as a base path.
+* Providing comments on where to find referenced data artefacts
+* Providing instructions on how to build referenced runtime libraries (in the case of Dynlib drops).
+
diff --git a/docs/architecture/reproducibility/reproducibility.rst b/docs/architecture/reproducibility/reproducibility.rst
index ee49207e9..9c601bde3 100644
--- a/docs/architecture/reproducibility/reproducibility.rst
+++ b/docs/architecture/reproducibility/reproducibility.rst
@@ -33,4 +33,5 @@ We refer a motivated reader to the
  rmodes
  blockdags
  helloWorldExample
+ graphcertification
  adding_drops
\ No newline at end of file