What Is Decision Retrieval?
Decision retrieval is how an AI agent looks up how similar situations were handled before — and gets back the reasoning, not just a snippet. It is GraphRAG for decisions: retrieval over a normative argument graph where each decision links to the arguments, evidence, and precedents that shaped it. Instead of a pile of matching log lines, you get a bounded, auditable decision with its reasoning intact.
Decision retrieval = retrieving past decisions as complete units, not text chunks. The substrate is a graph: decisions connected to pro/con arguments, evidence, and precedents by typed edges. Retrieval is hybrid — vector search finds the relevant decisions, graph expansion fetches the full context — and the output is a Decision Packet. It beats vector-only RAG over logs, which returns disconnected fragments that drop the counterargument and the precedent.
Decision retrieval vs. vector RAG over logs
The default way to give an agent memory is vector RAG: embed everything, retrieve the top matching chunks, and paste them into the prompt. For documents this works. For decisions it fails structurally, because a decision is not a paragraph — it is a small web of relationships: a proposition, the arguments for and against it, the evidence each leaned on, who approved any exception, and which past case it followed.
Flatten that into text and embed it, and retrieval hands back fragments with the relationships severed — the conclusion without the counterargument, the approval without the reasoning, a mention of a precedent without the precedent itself. Practitioners call this "chunk soup." Decision retrieval avoids it by retrieving the decision as a structured unit, because the relationships are stored as first-class edges rather than left implicit in prose.
The graph: normative edges, not descriptive ones
Generic knowledge graphs store descriptive edges — "mentions," "related to" — which say two things are connected but not why it mattered. A decision graph stores normative edges: supports, opposes, refutes, qualifies. Because the edge type is the reasoning, the graph already encodes what was load-bearing in a decision.
That is also why a decision graph is an unusually good GraphRAG substrate where general enterprise graphs struggle. Generic graphs are enormous, their edges are low-signal, there is no natural root node, and traversal has no obvious stopping point. A decision graph has the opposite properties: each decision is a natural root, edges carry real reasoning, and a single decision's subgraph is small and naturally bounded — expand to its arguments, evidence, and cited precedents, then stop.
How hybrid vector-plus-graph retrieval works
Vectors and graphs are not rivals here — decision retrieval uses vectors to find the entry points and graph structure to fetch the complete context. In practice it is a pipeline: (1) vector search over decision and argument embeddings finds a handful of relevant past decisions; (2) structured filters narrow by category, entity type, and precedent maturity so you retrieve validated cases, not drafts; (3) graph expansion walks the normative edges to pull in each decision's arguments and evidence; (4) outcome-weighted ranking floats precedents whose results turned out well above superficially similar ones that did not; (5) the results are packaged into bounded Decision Packets.
Hard limits on depth, node count, and time keep expansion from running away. The output is never a wall of tokens — it is a small set of complete, comparable decisions.
Retrieval for agents: MCP and A2A
Memory is only useful if an agent can reach it while reasoning. Decision retrieval is exposed to agents in two ways. Over the Model Context Protocol (MCP), tools such as search_precedents and get_packet let an agent query for relevant prior decisions and receive Decision Packets inline, mid-reasoning — and the write path works over MCP too: an agent can create a trace, record its reasoning, seal the decision, and immediately retrieve the resulting Decision Packet, end to end, with no SDK. In agent-to-agent (A2A) delegation, the Decision Packet is the payload passed between agents — a receiving agent inherits the full, self-contained context of a decision without any access to the sender's database. The packet is portable by design, which is what makes it safe to hand across a trust boundary.
How AI Agentree delivers decision retrieval
AI Agentree stores decisions as a normative graph and retrieves them as bounded packets. The retrieval substrate is shipped and in use:
Hybrid precedent search
A five-step pipeline — vector search, structured filters, graph-context expansion, outcome-weighted ranking, packaging — retrieves past decisions with their reasoning attached, not as chunk soup.
Decision Packets
Retrieval returns a bounded, self-contained record of a decision — proposition, pro/con arguments, evidence, policies, approvals, outcome, and cited precedents.
Precedent citation
A retrieved precedent can be cited as a first-class argument in a new decision, so consistency compounds instead of relying on memory.
Agent-native access (MCP + A2A)
Agents record and retrieve decisions over MCP tools — the full create, seal, and get-packet lifecycle with no SDK — and Decision Packets travel as the payload in agent-to-agent delegation.
See the substrate this retrieves from on decision tracing, or the deeper technical treatment in GraphRAG for AI decisions.
Frequently Asked Questions
What is decision retrieval?
Decision retrieval is retrieving past AI decisions as complete, structured units rather than disconnected text fragments. It is a form of GraphRAG: the substrate is a normative argument graph connecting each decision to the arguments, evidence, and precedents that shaped it, and the output is a bounded Decision Packet — the reasoning, not just a snippet.
How is decision retrieval different from vector RAG?
Vector RAG embeds text and returns the top matching chunks — disconnected fragments that can quote a decision's conclusion but miss the counterargument, the approver, and the precedent it relied on. Decision retrieval returns the decision with its reasoning intact, because the relationships are stored as first-class typed edges rather than left implicit in prose. Vector search is a component of decision retrieval, not a competitor to it.
What is GraphRAG?
GraphRAG (graph-enhanced retrieval-augmented generation) retrieves context by traversing a knowledge graph instead of only ranking text chunks by similarity. Applied to decisions, the graph is a normative argument graph, and retrieval is hybrid: vector search finds the relevant decisions, then graph expansion fetches the complete, bounded context.
What is a Decision Packet?
A Decision Packet is the bounded output of decision retrieval: a self-contained record of one decision containing the proposition, the pro/con argument tree, the evidence and its provenance, the policies evaluated, the approvals, the sealed outcome, and any precedents cited. It is what an agent or auditor retrieves instead of a pile of log lines.
Is a Decision Packet better than storing chain-of-thought output?
They solve different problems — the packet is better record-keeping, not better reasoning. Stored chain-of-thought is prose: querying it means re-parsing every trace with an LLM at read time, it has no outcome field to join on, and an editable text blob offers no commit point a reviewer can trust. A Decision Packet structures the same reasoning once, at seal: stance-typed pro/con arguments become deterministic graph queries, decisions link to recorded outcomes so retrieval is outcome-weighted, precedents become citable argument nodes in new decisions, and the hash-chained, sealed, signable record works as evidence — including across trust boundaries in agent-to-agent delegation. Neither format makes the model's reasoning more faithful; the packet makes the stated reasoning durable, comparable, and defensible. If you only need to debug an agent later, stored chain-of-thought is enough — packets earn their structure when decisions must be compared, reused, or defended.
Does decision retrieval replace my vector database or document RAG?
No. Vector search is the first step of the retrieval pipeline, and document RAG is a separate job. Decision retrieval sits on top of your existing stack and captures the one artifact those systems did not store: the structured decision itself, retrievable with its reasoning intact.
How does AI Agentree implement decision retrieval?
Through a shipped five-step precedent-search pipeline (vector search, structured filters, graph-context expansion, outcome-weighted ranking, packaging), Decision Packet assembly, precedent citation, and retrieval exposed to agents over MCP and A2A. Automatic outcome feedback and cross-decision pattern synthesis — the fully self-improving layer on top of retrieval — are on the roadmap.
Related AI governance topics
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Decision Tracing
Capturing the structured reasoning behind every AI decision — AI Agentree's category.
AI Precedent Systems
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Give your agents a memory worth retrieving
Store decisions as a graph, not a log — and retrieve them as bounded Decision Packets your agents can reason over.
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