AI Debugging Tools
See inside every step of a running agent. Inspect intermediate cached data, trace exactly what was passed between workflow stages, and pinpoint where multi-step logic deviated — without re-running computations or rebuilding agent state from scratch.
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Multi-step agents are nearly impossible to debug without the right tools
Standard debugging approaches — logs, print statements, re-runs — break down when agents carry state across steps, tools, and sessions.
The agent's working memory is invisible
Between each step of a multi-stage agent workflow, intermediate results are cached and passed forward as opaque references. Developers have no way to see what data the agent is actually carrying — its schema, its shape, its record count — without re-running the computation that produced it.
Debugging means re-running the whole workflow
When a step produces wrong output, the only way to inspect what went in and what came out is to re-execute the pipeline from scratch and add logging. This is slow, expensive, and doesn't work for non-deterministic or time-sensitive agent workflows.
No way to trace data across pipeline stages
In a multi-step agent — fetch data, filter it, transform it, pass it to a decision — the intermediate data state at each stage is invisible once the step completes. If the wrong records arrive at step 4, there's no record of what step 3 actually produced.
Misrouted workflows leave no forensic trail
When an agent takes a wrong branch — invoking the wrong tool, sending data to the wrong integration — there's no structured record of the decision chain. Developers are left inferring what happened from LLM-generated explanations, not actual execution data.
State inspection requires rebuilding context
Understanding an agent's current state requires querying multiple systems: log aggregators, memory stores, database records. There's no unified interface that shows the agent's working memory, cached datasets, and tool call history in a single view.
Intermediate data expires before it can be examined
Cached results from agent tool calls are typically ephemeral — they expire immediately or are discarded once consumed. By the time a bug is identified, the intermediate data that caused it is gone.
Four tools that give agents a debugger
Inspect working memory, trace data across pipeline stages, and verify workflow plans before they execute — all at zero or minimal cost.
Ephemerals
Inspect your agent's active working memory.
ephemerals.list · ephemerals.inspectEphemerals provides a managed view over every active cached dataset in your agent's session — zero cost, zero LLM, pure ETS lookups. ephemerals.list surfaces every cache_ref in-flight: source tool, record count, inferred schema, and time until expiry. ephemerals.inspect drills into any single entry for a full schema breakdown and sample rows.
- List all active cached datasets with source, shape, and TTL
- Inspect any cache entry — schema, sample rows, record count
- No re-running computations to see intermediate state
- Zero credit cost — both tools are free ETS lookups
cache_ref
Trace data as it flows through your pipeline.
universal pipeline patternEvery tool response in DataGrout includes a cache_ref in its _meta.datagrout metadata — a pointer to the encrypted, time-limited cached result. Agents pass cache_ref to subsequent tools instead of re-sending full payloads. This creates a traceable chain: you can follow exactly which data entered each step, without ever losing track of what was produced and consumed.
- Every tool response includes a cache_ref for tracing
- Subsequent tools reference prior results — no re-sends
- Cached results are AES-256-GCM encrypted at rest
- Touch-on-access TTL: 10 minutes of inactivity
Inspect
Full execution history for every agent run.
inspect.execution-history · inspect.execution-detailsInspect records every execution across every agent and workflow. inspect.execution-history returns the full run log for any agent. inspect.execution-details drills into a single run — every tool called, every argument passed, every result returned, in chronological order. Combined with cache_ref tracing via Ephemerals, you get both what ran and what data state the agent was operating on at each step.
- Full per-run tool call trace with timestamps
- Argument and result snapshots for every step
- Cross-agent execution history in one query
- CTC-verified workflow runs with cryptographic proof
Flow
Pre-execution validation so bugs surface before runtime.
flow.into · CTCsFlow validates multi-step agent workflows before any step executes: no cycles, type-safe variable references, policy compliance, credentials available. Every validated plan receives a Cognitive Trust Certificate. When something goes wrong, the CTC tells you what was verified before execution and what actually ran — giving you a before/after diff for debugging without manual instrumentation.
- Pre-execution Prolog validation catches type errors early
- CTC issued before and after every workflow execution
- Variable reference errors caught at plan time, not runtime
- Human approval gate records for sensitive steps
Debugging a misrouted multi-stage agent in six steps
An AI Engineer uses Ephemerals, cache_ref, and Inspect to find exactly where a workflow deviated — without re-running a single computation.
An agent misroutes a support request after classification
An AI Engineer is debugging a multi-stage agent that processes incoming support tickets. The agent classifies the request, then routes it to the appropriate tool. A specific class of ticket is consistently triggering the wrong tool — but the developer can't see what data the classifier produced before the router consumed it.
ephemerals.list surfaces every active cache_ref in the session
The developer calls ephemerals.list mid-session. It returns every cached dataset currently in-flight — including the classification result from step 1, its schema, record count, source tool, and time until expiry. Without re-running anything, the developer can see exactly what data the router received as input.
ephemerals.inspect reveals the schema and sample rows
The developer passes the cache_ref for the classification output to ephemerals.inspect. It returns the inferred schema, sample rows, and full record count. The developer can see that the classifier is producing a confidence score field the router isn't checking — it's routing on a field that's missing for this ticket class.
inspect.execution-details traces the full tool call sequence
The developer calls inspect.execution-details for the run. It returns every tool called — classification, normalization, routing — with exact arguments, results, and timestamps in order. The misrouting decision is now fully visible: the router received a confidence score of 0.48 but the branch condition required above 0.5.
Flow validates the corrected workflow before it re-runs
The developer updates the routing logic and defines the fixed workflow as a Flow plan. Before any step executes, Flow validates the plan: type-safe variable references, no cycles, policy compliance. A CTC is issued. The next run executes the corrected logic — and the CTC provides proof that the fix was verified before execution.
The full debug session is reproducible and auditable
Every inspection step — ephemerals queries, Inspect traces, and Flow validation — is recorded. The Engineering Manager can review the full debug session, confirm the root cause, and share the CTC with the team as evidence that the corrected workflow was verified. No more 'it works in my logs' — the execution record is the source of truth.
Who benefits and how
Deep agent debugging serves different stakeholders across the development and delivery lifecycle.
AI / Agent Engineer
- Inspect active cached datasets mid-session via ephemerals.list
- See exact schema and sample rows for any cache_ref
- Trace every tool call, argument, and result in execution order
- Identify the precise step and input where logic deviated
- Zero cost for Ephemerals inspection — free ETS lookups
Engineering Manager
- Full cross-agent execution history via Inspect
- Reproducible debug sessions with CTC-signed proof
- Pre-execution Flow validation catches bugs before they hit production
- Shareable execution traces for post-mortems and team reviews
- No need to rebuild agent state from log files
Solutions Architect / AI Product Manager
- Auditable data provenance for every pipeline stage
- cache_ref chain tracing shows exactly what data moved between steps
- CTC-verified workflows as reproducible, validated patterns
- Data lineage visible without custom instrumentation
- Debugging surface works across any MCP-compatible agent framework
AI Debugging in Action
Working memory inspection, execution tracing, and pre-validated fixes — across the full debugging lifecycle.
Pinpointing where a multi-stage pipeline passed wrong data
An engineer's document-processing agent is producing incorrect summaries for a specific document class. Using ephemerals.list, the engineer inspects the active cache at each stage mid-run and discovers that the normalization step is dropping a required field for PDFs with non-standard headers. The fix is isolated to that step — no need to re-run the entire pipeline.
Reproducible debugging across non-deterministic agent runs
A team is unable to reproduce an agent bug that only occurs under specific load. Using Inspect's execution history, they reconstruct the full tool call sequence from a prior failed run — exact arguments, results, and timestamps. The cache_ref chain shows that a timeout during data fetching caused a downstream step to receive a truncated dataset. No manual reproduction required.
Validated workflow before deploying a fix to production
After a root cause is identified, the manager requires proof that the corrected workflow was verified before going live. The team submits the fixed plan to Flow, which validates it with a Prolog check — type-safe, no cycles, policy-compliant — and issues a CTC. The CTC is attached to the deployment record as cryptographic evidence that the fix was verified before execution.
Data lineage audit for a complex cross-system agent pipeline
An architect needs to demonstrate that customer data flows correctly through a multi-step agent: fetched from Salesforce, normalized, filtered for PII, then pushed to a reporting system. The cache_ref chain through each step, combined with Inspect's execution trace, provides a complete, auditable data lineage record — showing exactly what data entered and exited each stage.
Frequently asked questions
Common questions about AI debugging tools, agent state management, and workflow introspection with DataGrout.
What are AI debugging tools?
AI debugging tools give developers visibility into the internal state and execution history of autonomous AI agents — particularly multi-step agents that carry data and state across pipeline stages. Unlike traditional debuggers that attach to running processes, AI debugging tools operate at the tool-call layer: inspecting cached intermediate results, tracing data as it flows through workflow stages, and reconstructing the exact decision chain that led to an unexpected outcome. DataGrout provides this through Ephemerals (working memory inspection), cache_ref (data flow tracing), and Inspect (full execution history).
What is AI agent state management?
AI agent state management refers to the mechanisms that track, persist, and make inspectable the data an agent carries across steps, sessions, and tool calls. In a multi-stage agent, state includes: the intermediate results cached between steps (accessible via cache_ref and Ephemerals), the facts and constraints stored in symbolic memory (Logic), and the execution history of prior runs (Inspect). Effective state management means an agent's working memory is never invisible — developers can inspect what the agent knows, what data it's carrying, and what it's about to do at any point.
What is a cache_ref and how does it help with debugging?
A cache_ref is a pointer to an encrypted, time-limited cached result produced by any DataGrout tool call. Every tool response includes a cache_ref in its _meta.datagrout metadata. Agents pass cache_ref to subsequent tools instead of re-sending full payloads, creating a traceable chain: you can see which data entered each step, what the output was, and which downstream tools consumed it. For debugging, cache_ref tracing — combined with ephemerals.inspect — lets developers examine exactly what data a step received and produced without re-running the computation that created it.
How does Ephemerals work?
Ephemerals provides two zero-cost tools for inspecting your agent's active working memory. ephemerals.list returns every cached dataset currently in-flight for the session: the source tool, record count, inferred schema, and time until expiry. ephemerals.inspect drills into any specific cache entry — returning the full inferred schema, sample rows, and record count. Both tools are pure ETS lookups — no LLM, no database, no network call — and cost zero credits. They expire alongside their source cache entries, so they reflect the live state of the agent at the time of the call.
How is this different from AI agent observability?
AI agent observability focuses on fleet-level health, uptime, loop detection, and high-level execution history — tools for engineering managers and operations teams asking 'is the agent running and is it healthy?' AI debugging tools focus on the developer-level question: 'why did this specific agent, at this specific step, produce this specific wrong output?' Observability tells you something went wrong; debugging tools tell you exactly where, what data was involved, and what the agent's state was at the moment of failure. DataGrout provides both capabilities — Governor and Inspect for observability, Ephemerals and cache_ref for debugging — within the same platform.
Can I inspect an agent's state without stopping it?
Yes. Ephemerals tools are non-intrusive — they read the agent's active cache without interrupting execution. ephemerals.list and ephemerals.inspect operate on ETS data that persists independently of the agent session, so calling them does not affect the agent's execution, alter its state, or consume any cached data. The touch-on-access TTL (10 minutes of inactivity) means inspecting a cache_ref resets its expiry clock, but does not invalidate or alter it.
How does Inspect complement Ephemerals for debugging?
Ephemerals answers 'what data does the agent have right now?' — it's for live, in-session inspection of working memory. Inspect answers 'what did the agent do and what data did it use?' — it's for post-run forensic analysis. inspect.execution-history returns the full run log. inspect.execution-details drills into one run, showing every tool called, every argument, every result, and every cache_ref produced and consumed, in chronological order. Together, Ephemerals and Inspect give you both real-time state visibility and a complete retrospective trace — the two views you need to debug any agent failure.
What agent frameworks and clients does this work with?
DataGrout's debugging tools work with any MCP-compatible agent framework — Claude, Cursor, LangChain, n8n, custom Python or TypeScript agents via the Conduit SDK, and any client that speaks JSON-RPC 2.0. Ephemerals and Inspect are part of DataGrout's built-in tool suite, accessible through the same MCP endpoint as all other integrations. The Conduit SDK (available in Python, TypeScript, Rust, Elixir, and Ruby) provides namespaced wrappers — client.ephemerals.list, client.ephemerals.inspect — for native access.
Ready to see inside every agent step?
Working memory inspection, execution traces, and pre-validated fixes — all from one platform. Stop guessing; start debugging.
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