LLM Observability Tools
DataGrout gives engineering teams full visibility into every LLM token, cost, and call — real-time tracking of token consumption, pre-execution cost estimates, itemized receipts, loop detection, and measurable token savings, without instrumenting a single API call.
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Why LLM observability is a production crisis
Without token-level visibility, engineering managers are flying blind on cost, performance, and risk.
No visibility into token burn
Engineering teams have no way to attribute token costs to specific agents, workflows, or API calls. Monthly LLM bills arrive as a single inscrutable number.
Runaway loops burning budget overnight
A single misbehaving agent can exhaust an entire monthly budget in hours. Without loop detection, the only signal is a surprise invoice.
No pre-execution cost estimates
Plans execute and the cost is only visible after the fact. There's no mechanism to reject or warn on expensive operations before they run.
Opaque model routing decisions
Whether an agent uses a $0.001 reflex or a $0.03 Opus call is invisible. Zero ability to audit whether expensive inference was actually warranted.
No token savings measurement
Teams optimize prompts blind — no baseline of what tokens were saved by caching, symbolic routing, or BYOK discounts vs. what was actually consumed.
Seven tools. Complete LLM visibility.
Token-level cost tracking, pre-execution estimates, loop detection, quality measurement, and cryptographic audit trails — all without touching your LLM provider's API.
Governor — Continuous Cost Cognition
governor.statusReal-time dashboard of uptime, credits consumed, token savings, and scheduled tasks. Governor splits LLM calls into zero-cost symbolic reflexes and high-value neural reflection — so you can see exactly when expensive inference fired and why.
- Live credits consumed vs. token savings
- Reflex vs. Reflection ratio over time
- 10-100x token reduction — measurable
- Auto-enriches every tool call with facts
Credits & Receipts — Itemized LLM Accounting
credits + receiptsEvery tool call produces a receipt: estimated vs. actual credits, net credits consumed, savings, BYOK discounts, and per-component breakdowns for composite calls. Budget limits reject plans before they execute.
- Pre-execution credit estimates per step
- Itemized receipts after every call
- BYOK discount reflected in receipts
- Per-session and per-period budget caps
Inspect — Full Execution History
inspect.execution-historyQuery the complete run history of any agent or workflow. Drill into individual executions with inspect.execution-details to see every tool called, every token spent, and CTC-verified proof of what ran.
- Searchable execution history across all agents
- Per-run token and credit breakdown
- CTC-verified execution receipts
- inspect.ctc-executions for skill audit trails
Cadence — Intelligent Loop Detection
policyConsequence-aware session tracking detects when agents are looping before they exhaust your budget. Distinguishes legitimate repeated reads from destructive spirals. Hard blocks destructive operations after first call per session.
- Monotonic state counter detects zero-progress loops
- Read / Write / Destructive consequence tiers
- Time-window backstop as secondary defense
- Auto-halts before budget damage occurs
Cognitive Trust Certificates — Proof of What Ran
CTCsEvery verified workflow is sealed with an Ed25519-signed CTC. Post-execution, the certificate records which steps ran, budget sufficiency, drift detection, and statistical profiles. Shareable with no account required.
- Ed25519-signed, tamper-evident receipts
- Compile-time budget and policy proof
- Runtime execution proof per step
- Drift detection across repeated runs
Ephemerals — Cache Visibility
ephemerals.listSee every active cached dataset your agents are holding. Ephemerals shows what's in-flight, its shape, record count, source tool, and time to expiry — so you understand exactly what data your LLM is avoiding re-fetching.
- List all active cache entries for a session
- Preview schema and record count
- Track cache_ref chains across tools
- Zero cost — pure ETS lookups
Flux — Quality Measurement & Feedback
flux.feedback + flux.scorecardCapture structured feedback on LLM output quality without a separate evaluation platform. flux.feedback records NPS, ease-of-use, trust_level, and surprise_score per session. flux.scorecard tracks quality before and after prompt or model changes — making improvement measurable.
- NPS, trust_level, and surprise_score per session
- flux.scorecard for A/B model and prompt comparison
- flux.track for custom telemetry at workflow checkpoints
- Links quality signals directly to run_ids and cache_refs
From "why is our LLM bill so high?" to root cause in minutes
An engineering manager overseeing a fleet of customer support agents spots anomalous token costs.
Engineering manager checks Governor dashboard
governor.status reveals one agent cluster consumed 4× more credits than expected. Token savings from symbolic reflexes dropped from 85% to 12% — a signal that something forced full neural reflection repeatedly.
Inspect surfaces the problematic run
inspect.execution-history filters by the agent ID and time window. inspect.execution-details on the flagged run shows the same Salesforce query called 47 times with identical parameters — Cadence loop detection had already logged it.
Credits & Receipts quantify the damage
Each receipt shows estimated vs. actual credits per call. The manager sees $340 in excess spend from the loop — with per-step breakdown confirming which workflow step caused the spiral.
CTC audit confirms what ran
inspect.ctc-executions returns Ed25519-signed certificates for every workflow invocation. Drift detection in the certificate flagged that this run diverged from the baseline statistical profile after call 3.
Budget cap applied, problem resolved
A per-session budget cap is configured via Credits & Receipts. Future runs exceeding the threshold are rejected before execution, not after. Governor's symbolic rules are updated to detect the pattern at reflex cost.
Who benefits and how
Engineering Manager
- Token cost attribution per agent, workflow, and time window
- Pre-execution budget gates — no surprise overages
- Loop detection halts runaway agents automatically
- Shareable CTC audit links for incident post-mortems
- 10-100x token reduction with measurable proof
AI Agent Developer
- inspect.execution-details for deep per-run debugging
- cache_ref tracking shows what data is being reused
- Reflex vs. Reflection ratio guides prompt optimization
- BYOK discount reflected in every receipt
- Ephemerals shows in-flight cached datasets
CIO / Finance
- Itemized LLM spend per team, agent, and period
- Budget cap enforcement before spend occurs
- Cryptographic receipts for compliance and audit
- Forecasting via credit consumption trends
- BYOK program for cost reduction at scale
Frequently asked questions
What are LLM observability tools?
LLM observability tools give engineering teams visibility into the token consumption, cost, latency, and behavior of large language model calls made by their applications or AI agents. They surface which calls are expensive, whether loops are occurring, and whether budgets are being respected — before and after execution.
How does DataGrout provide LLM observability without touching the LLM provider's API?
DataGrout sits between your agents and your tools as a governed execution layer. Every tool call — including those that trigger LLM inference — passes through DataGrout's credit and receipt system. Governor tracks cognitive cycles, Credits & Receipts produce itemized cost records, and Inspect logs every execution. No API instrumentation or SDK changes required on the LLM provider side.
What is the difference between a Governor Reflex and a Reflection?
A Reflex is a zero-token, sub-10ms symbolic evaluation running every ~30 seconds — Prolog rules evaluated against the fact database. A Reflection is a full LLM inference call that fires when a trigger matches. Governor.status shows the ratio of Reflexes to Reflections over time, giving you a direct measure of how much expensive inference is actually warranted.
Can I set budget limits to prevent runaway LLM spend?
Yes. Credits & Receipts supports per-operation, per-session, and per-period budget caps. Plans that would exceed a budget threshold are rejected before execution — not after the tokens are spent. You can also configure Cadence's loop detection to halt agents before they spiral through expensive repeated calls.
What are Cognitive Trust Certificates and how do they help with LLM observability?
CTCs are Ed25519-signed cryptographic certificates issued before and after workflow execution. Post-execution, they record which steps ran, actual credit consumption, budget sufficiency, and drift from the expected statistical profile. They give you a tamper-evident, shareable record of exactly what your agent's LLM calls did — useful for incident post-mortems, compliance audits, and cost attribution.
Does DataGrout work with any LLM provider?
DataGrout is provider-agnostic. It connects to your tools via MCP or JSON-RPC and instruments the calls at the DataGrout layer, regardless of whether the underlying tool uses OpenAI, Anthropic, Google, or any other model. Governor's token savings and cost tracking apply across all providers.
How does BYOK (Bring Your Own Key) affect cost tracking?
When you provide your own LLM API keys, DataGrout applies a discounted credit rate and reflects the savings in every receipt. This gives you both the cost efficiency of direct-provider pricing and the full observability of DataGrout's itemized accounting — best of both.
Ready to see exactly what your LLMs are doing?
Token-level cost visibility, pre-execution estimates, loop detection, and cryptographic receipts — all from the DataGrout platform.
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