AI Agent Observability Tools
See inside every agent, every decision, every run. DataGrout gives engineering teams and agent developers full observability across the entire agent lifecycle — tool use, workflow execution, memory state, uptime, loop detection, and cryptographic proof of what actually ran.
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The AI agent observability gap
Traditional APM and LLM monitoring tools were built for stateless services. Autonomous agents are fundamentally different.
Agents fail silently
When an agent gets stuck, halts, or produces wrong output, there's no execution trace to explain why. Debugging means re-running the agent and hoping it fails the same way.
No uptime or health visibility
Engineering teams have no way to know if long-running agents are alive, healthy, or have stalled. There is no equivalent of a health check or liveness probe for autonomous agents.
Memory is invisible
Facts agents have asserted, constraints they've set, and context they're carrying across sessions are inaccessible. Debugging agent behavior requires guessing what the agent 'knows'.
Tool use is unaudited
Which tools an agent called, in what order, with what arguments — all invisible. When an agent takes an unintended action, there's no record of the decision chain that led there.
Loops go undetected until they're catastrophic
Agents stuck in behavioral loops continue executing until budget is exhausted, rate limits are hit, or a human notices. There's no mechanism to detect and halt spiraling behavior.
Workflows can't be verified in advance
Multi-step agent plans execute speculatively. There's no way to confirm a plan is type-safe, policy-compliant, and terminates before it runs — only after it succeeds or fails.
Eight layers of agent observability.
Health, execution history, memory state, workflow verification, loop detection, in-flight data visibility, behavioral measurement, and persistent output records — all in one platform.
Governor — Agent Health & Uptime
governor.statusContinuous cognition tracking for long-running agents. governor.status surfaces uptime, active sessions, scheduled tasks, last heartbeat, credits consumed, and token savings. The Reflection/Reflex cycle gives you a window into cognitive load over time.
- Real-time uptime and session health
- Scheduled task status and next-run times
- Passive fact enrichment — every call auto-logged
- Detect stalled agents via heartbeat gaps
Inspect — Full Execution History & CTC Verification
inspect.execution-historyQuery every execution across every agent and workflow. inspect.execution-history returns the full run log. inspect.execution-details drills into one run — every tool called, every argument passed, every result returned, in order. inspect.ctc-executions surfaces CTC-verified skill runs.
- Complete per-run tool call trace
- Argument and response snapshots per step
- CTC-verified workflow audit trail
- Cross-agent execution history
Logic — Memory & Knowledge Observability
logic.reflectSee exactly what your agent knows. logic.reflect audits the full fact space for any entity — every asserted fact, every active constraint, every rule governing agent behavior. logic.query interrogates the knowledge base in natural language without touching the LLM.
- Full fact space audit per entity or namespace
- Active constraint and rule inspection
- Multi-agent shared fact visibility
- Namespace isolation — zero cross-agent bleed
Flow — Workflow Verification Before Execution
flow.into + CTCsMulti-step agent workflows validated by Prolog before they run: no cycles, type-safe variable references, policy compliance, and credentials available. Every verified plan receives a CTC. flow.route's conditional branches are auditable before and after dispatch.
- Pre-execution Prolog validation of full plan
- CTC issued before any step executes
- Conditional branch trace via flow.route
- Saved skills create reusable, audited patterns
Cadence — Behavioral Loop Detection
policy / cadenceConsequence-aware session tracking detects when agents are looping before the damage is done. Monotonic state sequence counter distinguishes legitimate repeated reads from zero-progress spirals. Write and destructive operations have automatic hard blocks.
- Consequence-tier classification per tool call
- State sequence counter — loop ≠ legitimate repetition
- Hard block on destructive loops after first call
- Time-window backstop as second defense layer
Ephemerals + Warden — In-Flight Data & Security State
ephemerals.list + wardenephemerals.list exposes every cached dataset in a session — source, shape, expiry — so you can see what data is moving through your agent pipeline at any moment. Warden ensemble tracking surfaces active threat classifications and multi-turn suspicion accumulation.
- In-flight cached dataset visibility
- cache_ref chain tracing across tools
- Multi-turn prompt injection suspicion scores
- Warden confidence scores per interaction
Flux — Behavioral Measurement & Feedback
flux.feedback + flux.trackFlux captures structured feedback and custom event telemetry at the tool layer. flux.feedback records NPS, ease-of-use, and trust scores per session. flux.track emits events at workflow checkpoints — skill_invoked, approval_requested, threshold_crossed — giving observability into agent behavior over time.
- Session-level NPS, ease-of-use, and trust ratings
- Custom event telemetry at any workflow checkpoint
- flux.scorecard for before/after improvement tracking
- Links feedback directly to cache_refs and run_ids
Deliverables — Persistent Output Registry
deliverables.registerDeliverables preserve significant agent outputs — reports, analysis results, audit artifacts — beyond cache TTL. Every deliverable records the producing agent, run_id, and tool provenance. Query with natural language to find prior outputs across all agents in the workspace.
- Permanent, encrypted artifact storage
- Semantic search across all registered outputs
- Full agent, run_id, and tool provenance
- Compliance-ready chain-of-custody records
From "agents are slow and nobody knows why" to full situational awareness
An engineering manager overseeing a customer support agent fleet gets complaints about slow ticket resolution.
Slow response alerts — governor.status is checked first
governor.status reveals that three agents in the support cluster have had no heartbeat in 47 minutes. Scheduled tasks queued behind them are pending. The manager identifies which agents are stalled without looking at a single log file.
Inspect surfaces the stuck workflow
inspect.execution-history filters for the stalled agent IDs. inspect.execution-details on the last recorded run shows the agent reached flow.request-approval and halted — waiting for human sign-off that was never routed correctly.
Logic reveals what the agent was carrying
logic.reflect on the agent's namespace shows it had asserted 12 facts about the pending ticket — including a constraint blocking escalation until approval was received. The knowledge state explains exactly why the agent was frozen.
CTC confirms the workflow was safe to run
inspect.ctc-executions shows the CTC issued at plan validation: type-safe, policy-compliant, deterministic. The plan was verified correctly. The issue was an approval routing misconfiguration — not an agent error.
Cadence detects a second, unrelated agent looping
During the investigation, Cadence's loop detection surfaces a separate agent making 31 consecutive identical read calls. The state sequence counter confirms zero-progress. The agent is automatically halted before the spiral consumes budget.
Who benefits and how
Engineering Manager
- Fleet-level agent health and uptime via governor.status
- Instant root cause via inspect.execution-details
- Loop detection halts runaway agents automatically
- Shareable CTC audit links for post-mortems
- Scheduled task queue visibility across all agents
AI Agent Developer
- Full per-run tool call trace with arguments and responses
- Logic memory inspection without rebuilding agent state
- Pre-execution workflow validation via Prolog + CTCs
- cache_ref chaining visibility across tool pipeline
- Warden multi-turn suspicion scores for security debugging
CIO / Security Team
- Cryptographic execution proofs for every agent action
- Policy enforcement audit trail per workflow
- Human approval gate records for sensitive operations
- Dynamic redaction audit — what PII was masked when
- CTC viewer shareable with auditors — no account needed
Frequently asked questions
What are AI agent observability tools?
AI agent observability tools give engineering teams and developers visibility into the full lifecycle of autonomous agents — including health and uptime, workflow execution traces, memory state, tool call sequences, loop detection, and security posture. Unlike LLM observability, which focuses on token-level model calls, agent observability encompasses the entire system the LLM operates within.
How is AI agent observability different from LLM observability?
LLM observability focuses on the model call: tokens consumed, latency, cost per inference. AI agent observability is broader — it covers what the agent decided to do, which tools it called in what order, what state it's carrying across sessions, whether it's stuck, and whether its behavior aligns with its stated goal. DataGrout provides both, but through distinct layers: Credits & Receipts for LLM-level cost visibility, and Governor + Inspect + Logic for full agent behavioral observability.
How does DataGrout track agent uptime and health?
Governor maintains a continuous cognition session for each agent. governor.status returns real-time uptime, last heartbeat, active scheduled tasks, and session health. When an agent stalls — for example, at a human approval gate — the heartbeat gap becomes visible without any external health check infrastructure.
Can I see what an agent 'knows' at any point in time?
Yes. Logic is DataGrout's persistent symbolic memory layer. logic.reflect audits the full fact space for any entity or namespace — every asserted fact, every active constraint, every rule the agent has stored. Because facts persist across sessions and are shared across agents on the same server, you get a real-time view of agent knowledge state without rebuilding it from logs.
How does DataGrout detect agent loops?
Cadence, DataGrout's intelligent loop detection, uses a monotonic state sequence counter to distinguish between zero-progress loops and legitimate repeated operations. For write operations, it allows one call and gates on a second. For destructive operations, it hard-blocks after the first per session. This fires automatically — no configuration required — and halts the agent before damage occurs.
What is a Cognitive Trust Certificate (CTC) and why does it matter for observability?
A CTC is an Ed25519-signed cryptographic proof issued before and after workflow execution. It records what the plan was, what assurances were verified (termination, type safety, policy compliance), and post-execution, what actually ran (execution proof per step, budget sufficiency, drift detection). It's a tamper-evident execution record — the difference between 'the agent said it did X' and 'there is cryptographic proof X ran as described'.
Does DataGrout support multi-agent observability?
Yes. Logic's fact space is shared across all agents on the same DataGrout server (with namespace isolation). Governor tracks sessions per agent. Inspect's execution history spans all agents, filterable by agent ID, skill, or time window. You can observe an entire fleet from a single interface.
Does this replace traditional APM tools?
DataGrout is complementary to traditional APM. APM tools observe service infrastructure — latency, error rates, throughput. DataGrout observes agent cognition — what decisions were made, what tools were called, what the agent knows, whether behavior is safe and policy-compliant. For production AI agent systems, you need both.
Ready to see inside every agent?
Fleet health, execution traces, memory state, loop detection, and cryptographic proof — all from DataGrout.
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