ToolRuntime no custom state is provided looks like a framework nuisance. It is not. It is the agent equivalent of a request handler losing its session object after authentication and then trying to continue as if nothing important changed. The immediate symptom is usually narrow: a tool reads runtime.state, expects a field such as tenant_id, authz, trace_id, cart, case_id, or should_count, and receives only default message history or an empty state object. The cost is not the exception. The cost is every run that continues with the wrong identity, the wrong authorization boundary, the wrong memory, or the wrong tool routing decision.
That cost compounds fast. A single state injection failure can burn a full debugging day because three surfaces appear plausible at the same time: the tool signature, the graph state schema, and the invocation path. The model may still call the correct tool. The tool may still execute. The agent may still return a friendly answer. Nothing in that chain proves that custom state made it into the tool. If the only verification is a green response from the model, the system can be wrong for weeks while looking operational.
Milo treats this as a deterministic runtime-contract failure, not a prompt problem. A prompt cannot repair a missing injected object. Better instructions cannot make runtime.state["tenant_id"] appear if the graph compiled with the wrong schema, the agent was invoked without the expected state fields, a custom args_schema hid injected parameters, or a wrapper stripped runtime metadata before the tool node executed. The fix is to make the state contract explicit, write probes that fail before business logic runs, and ship a repeatable failure-forensics packet that distinguishes framework behavior from local wiring defects.
The target state is simple: every tool that depends on custom state gets the same object shape during local tests, integration tests, replayed failing traces, and production traffic. Every missing field fails loudly with a typed diagnostic. Every state-mutating tool writes through a controlled path such as Command(update=...) instead of mutating a copy and hoping the graph notices. The work is not glamorous. It is the difference between an autonomous agent that can safely execute work and one that silently drops its operating context.
ToolRuntime is valuable because it gives a tool a single explicit interface for runtime-only data. The model should see business arguments such as text, query, order_id, or limit. It should not see internal plumbing such as runtime, callback config, checkpoint identifiers, stream writers, stores, or tool-call IDs. A healthy tool signature can look like def count_characters(text: str, runtime: ToolRuntime) -> int. The model supplies text. The runtime injects runtime. The tool reads state and decides whether the operation is allowed or how it should behave.
The contract has three separate channels that engineers often blur together. State is mutable short-term graph data: messages plus custom fields needed during the conversation or run. Context is immutable per-invocation configuration: user identity, deployment lane, feature flags, or request metadata that should not be rewritten by a tool. Store is longer-lived memory: records that survive across conversations and should be namespaced and persisted deliberately. The error appears when code assumes one channel is present while the graph supplied another channel, or supplied only default state.
A minimal state contract should be named and tested, not implied. In Python terms, the contract might be class AgentState(AgentState): tenant_id: str; trace_id: str; authz: dict; messages: list, or a TypedDict with messages: Annotated[list, add_messages] plus the custom fields. The exact base class depends on the agent stack, but the invariant is stack-independent: the graph compile step, the agent constructor, and the invoke payload must agree on the state shape.
The tool must also avoid hiding runtime injection behind a custom schema. A risky pattern is @tool(args_schema=CountArgs) paired with def count_characters(text: str, runtime: ToolRuntime) while CountArgs only defines text. In some framework versions and wrappers, custom input schemas have been part of injection failures because the wrapper treats the schema as the complete call surface. The safe engineering posture is not to argue with the abstraction. Add a regression test for the exact decorated tool shape used in production. If the custom schema breaks injection, remove it, upgrade the framework, or create a wrapper that preserves injected parameters while keeping them hidden from the model-facing schema.
The fastest way to waste time is to start with package upgrades. The right sequence is to classify the failure before changing dependencies. Milo uses four buckets.
runtime.state["should_count"], but the compiled graph state only defines messages. The tool is behaving correctly; the graph contract is wrong.{"messages": [...]} and never supplies should_count, tenant_id, or the other required custom fields. This often appears only in tests, cron jobs, replay workers, or background entrypoints.args_schema, MCP bridge, or dynamic tool registry converts the function into a structured tool and drops injected runtime parameters. The model-facing schema looks clean, but the executable function no longer receives the hidden object.runtime.state in place or returning a plain string. The next node sees stale state because the graph expects an explicit state update object.The classification can be done with one probe tool. Add def runtime_probe(runtime: ToolRuntime) -> dict and return a sanitized shape, not raw secrets: {"state_keys": sorted(runtime.state.keys()), "has_trace_id": "trace_id" in runtime.state, "has_context": runtime.context is not None, "tool_call_id": bool(runtime.tool_call_id)}. Run it through the same graph path as the failing tool. Do not call the Python function directly; direct calls prove nothing about runtime injection. The probe must execute as a tool call inside the graph.
The expected result is boring. If state_keys is only ["messages"], custom state never reached the tool. If state_keys contains the custom fields locally but not in production, the problem is an entrypoint or deployment wrapper. If state_keys is correct but the business tool fails, the bug is inside the tool logic, not injection. This split matters because it prevents broad rewrites. A missing runtime object is fixed at the graph boundary. A stale update is fixed at the tool return boundary. A permission bug is fixed in the policy layer.
The first permanent artifact is a state contract module. It should live where graph construction and tools can import it without circular dependencies. The fields should be concrete, not a loose dict[str, Any] everywhere. A useful pattern is RequiredAgentState for fields that must always exist and OptionalAgentState for fields that may appear after a tool update. Required fields should include only what every runtime path can actually supply. If case_id exists only after a lookup tool runs, it is not required initial state.
The second artifact is a constructor that accepts the contract once. For example: agent = create_agent(model=model, tools=tools, state_schema=RuntimeState, context_schema=RequestContext). For lower-level graphs, the equivalent is workflow = StateGraph(RuntimeState) followed by tool-node registration against the same tool list used in production. The important part is eliminating duplicate anonymous schemas. If one file defines GraphState, another defines ToolState, and a third builds an invoke payload from an untyped dictionary, the bug will return.
The third artifact is an ingress normalizer. Every external caller should pass through a function such as build_initial_state(request). That function should create {"messages": messages, "tenant_id": tenant_id, "trace_id": trace_id, "authz": authz}, validate missing required fields, and attach a deterministic diagnostic before invoking the agent. A failing request should die at ingress with missing_runtime_state: tenant_id, not inside a tool after the model has already spent tokens deciding what to call.
The fourth artifact is a state assertion helper used inside sensitive tools. The helper can be small: require_state(runtime, "tenant_id", "trace_id", "authz"). It returns a typed view or raises a controlled exception that becomes a tool message with the run ID and missing keys. This is not defensive noise. It is the only way to prevent a tool from silently using defaults when the runtime contract is broken. A default such as tenant_id = "public" is acceptable only for a public read-only tool that has an explicit test proving the fallback is intended.
The fifth artifact is the update path. If a tool changes state, it should return the graph's state-update primitive, not a mutated local object. A state-setting tool should return something equivalent to Command(update={"selected_case_id": case_id, "messages": [ToolMessage(content="case selected", tool_call_id=runtime.tool_call_id)]}). The message matters because the model needs a tool result. The update matters because the graph needs a state transition. Conflating those two is how agents appear to remember inside one function and forget at the next node.
The regression suite needs three layers. Unit tests validate the helper functions, but unit tests alone are weak because ToolRuntime is injected by graph execution. The real protection is an integration test that forces the model step or a synthetic AI message to call the probe tool through the tool node. The assertion should read like a contract: assert "tenant_id" in probe_result["state_keys"], assert probe_result["has_trace_id"] is True, and assert probe_result["tool_call_id"] is True.
A second test should exercise the exact production decorator shape. If production uses @tool(parse_docstring=True), test that. If production uses @tool(args_schema=SearchArgs), test that. If tools are loaded dynamically, test the dynamic registration path. Many runtime bugs hide in the adapter layer between a normal Python function and the executable structured tool. A passing test against an undecorated function is irrelevant when the production system executes the decorated object.
A third test should replay the failing invocation. Capture the smallest safe payload: initial state keys, context shape, config keys, requested tool name, tool args, and package versions. Strip secrets. Then replay it locally through the compiled graph. The test should fail on the old code and pass on the fixed contract. If the failure cannot be replayed, the sprint has not produced forensics; it has produced a guess.
Observability should be equally plain. Log state_keys, not state values. Log context_type, not private context content. Log tool_name, tool_call_id, thread_id, run_id, package versions, and the graph entrypoint. This is enough to distinguish runtime injection loss from business logic failure without leaking sensitive payloads. The diagnostic should be emitted at the first missing-state boundary. Waiting until a downstream API call fails turns a one-line contract bug into a distributed trace archaeology problem.
Day one is capture and reproduction. Inventory every tool that reads runtime.state, InjectedState, context, store, or config. Mark the ones that authorize actions, select tenants, route data, mutate state, or call external systems. Add the runtime probe tool. Reproduce the failure inside the graph, not with direct function calls. The day ends with a failing test and a bucket assignment: schema, invocation, wrapper, or mutation.
Day two is contract consolidation. Create the canonical state and context definitions. Remove duplicate local state classes. Make graph construction use the canonical schema. Add the ingress normalizer and fail-fast validation. Replace implicit defaults with explicit required fields unless a fallback is proven safe. The day ends when every entrypoint invokes the agent through the same state-building path or has a documented reason not to.
Day three is tool repair. Update sensitive tools to use ToolRuntime consistently. Remove decorator patterns that break injection or wrap them with tests that prove they preserve runtime parameters. Add require_state checks to tools that depend on custom fields. Convert state-mutating tools to return explicit update objects with tool messages. The day ends when the old failure replay passes and the probe shows the expected state keys.
Day four is regression and observability. Add integration tests for static tools, dynamic tools, replay workers, background jobs, and any server path that invokes the graph differently from local development. Add sanitized runtime-contract logging. Add a negative test that intentionally omits tenant_id or trace_id and asserts a controlled diagnostic. The day ends when the suite can prove both success and failure behavior.
Day five is rollout. Ship behind a narrow lane if the agent touches real accounts, private data, or external side effects. Watch the contract logs first, business metrics second. A business metric can look normal while state is being dropped for a minority of entrypoints. The release is complete only when production traces show custom state arriving at the tools that require it, missing-state diagnostics are rare and explainable, and replay artifacts are committed beside the tests.
The practical lesson is blunt: ToolRuntime no custom state is provided is not solved by telling the model to be more careful. The model is not the component responsible for injecting hidden runtime data. The repair belongs at the graph boundary, tool wrapper boundary, and state update boundary. When those boundaries are executable contracts, the error becomes easy to reproduce, easy to classify, and hard to reintroduce.
Milo recommends treating this as a five-day failure-forensics sprint because the real deliverable is not one patched stack trace. The deliverable is a working reproduction, a canonical state contract, repaired tool signatures, update-path tests, sanitized diagnostics, and a rollout record that proves the fix reached the runtime path that was failing. That package pays for itself the next time an agent loses context, calls a tool with stale memory, or returns a clean answer from a broken execution path.
If the current system is already showing this error, the next move is not another ad hoc debug session. Run the internal sprint that is built for exactly this class of failure: Agent Failure Forensics. It turns a vague runtime complaint into a replayable incident, a bounded patch set, and regression coverage that keeps custom state available where the tools actually need it.
Five business days, fixed price, full runbook on delivery. Sample deliverables on the sprint page show exactly what you get before you commit.
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