Bring your own models. Activate your Trust Agent.
Inspect, replay, and explain AI runtime behavior. Recon stays above your stack with replay-safe evidence and operator-guided posture—not autonomous governance or generic chat.
One trust layer across all of them. BYO Runtime overview →
Seeded links use guided workspace org_demo_legal · sign in for your tenant data
Continuity overlays on a runtime topology—not a conversion funnel. Replay highlights the edges where trust moves.
Live topology · 14 recent trust-state edges · fleet sample (same pattern as TrustGraph runtime)
Trust topology from database sample: agents on the left connect through runtime to tools on the right, showing up to 10 recent edges.
- Continuity shiftReplay-derived
Topology detects posture drift across runtime edges
- Verifier posture changeReplay-derived
Receipt scope tightens against policy-bound evidence
- Recovery pivotReplay-derived
Replay selects a bounded continuation path
- Portable continuity attestationReplay-derived
Cross-runtime bundle anchors verifier exchange
Topology continuity reads as interoperability posture across surfaces—synthetic replay-derived echoes for illustration, not wire authority or live enforcement.
An inspection companion—not a chatbot shell
Every workspace gets a scoped Trust Agent for replay guidance, GhostLog interpretation, trust timelines, and mission lineage—Starter stays inspect-only (no autonomous actions). Aliethia stays on first contact and mission framing; the Trust Agent stays tied to runtime evidence and replay posture.
- Inspect-only Starter posture
Guides install, first replay, and receipt vocabulary without taking action on your behalf.
- Replay narration
Points to where execution diverged and what evidence exists—forensic language, not assistant banter.
- GhostLog & timeline
Surfaces lineage you can defend; summaries stay tethered to traces, not detached prose.
- Trust scores in context
Interprets Reflex and RTS as operational signals inside your workspace—not generic model judging.
- Mission continuity
Flags incomplete or broken lineage so operators fix posture before failures compound.
- Tier-scoped agency
Higher tiers add drift watch and guided repairs; moves stay policy-bound and receipt-backed.
Activation is only useful when the proof is replay-safe.
These visible receipts turn runtime behavior into inspectable evidence: GhostLog artifacts, drift posture, replay availability, and Trust Agent summaries that read like operations records instead of chat transcripts.
Replay sequence visual
- 1T+00:00Runtime action
Agent selected refund.lookup and prepared tool payload for mission RCN-042.
Captured - 2T+00:04GhostLog capture
Input, tool intent, output hash, and operator scope sealed into GhostLog.
Stable - 3T+00:09Drift detection
Trust delta crossed review threshold after endpoint mismatch surfaced.
Drift detected - 4T+00:12Replay available
Bounded replay package ready with trace context and evidence pointers.
Replay ready - 5T+00:15Trust Agent summary
Runtime posture summarized for operator review without autonomous action.
Review queued
Trust Agent · Runtime Posture
- Runtime State
- Instrumented / trace accepted
- Drift Status
- Endpoint mismatch under review
- Replay Availability
- Bounded replay packet ready
- Trust Score
- 82 / stable with review flag
- Mission Continuity
- Lineage intact across handoff
- GhostLog18:22:09
Entry GL-1182 sealed tool intent, payload hash, and response envelope.
- Lineage18:22:13
Mission RCN-042 maintained parent trace through refund.lookup call.
- Drift event18:22:17
Endpoint selection diverged from operator-approved path by one hop.
- Replay18:22:24
Packet RPL-7A9 generated with redacted inputs and verifier notes.
GhostLog snapshot
Compact signature artifact for replay review.
- Timestamp
- 2026-05-14 18:22:09 UTC
- Runtime
- LangChain / OpenAI tools
- Mission
- RCN-042 customer refund lookup
- Tool
- refund.lookup -> customer.record
- Trust delta
- -18 pts after endpoint mismatch
- Replay available
- Yes / bounded packet RPL-7A9
Runtime trust timeline
Timestamped event lines for forensic replay posture.
Mission RCN-042 entered runtime with operator-approved refund lookup scope.
Runtime action captured: refund.lookup selected with customer.record target.
GhostLog sealed input envelope, tool intent, response hash, and lineage pointer.
Drift detected: selected endpoint deviated from approved mission path.
Replay package generated and marked available for operator review.
Trust Agent posted runtime posture summary with no autonomous remediation.
AI systems should be replay-native by default.
Replay-native systems keep runtime behavior inspectable over time: lineage, checkpoints, drift posture, and recovery context. Recon frames that continuity as static public posture here, not live telemetry.
What happens without replay?
- •Broken lineage hides where behavior changed.
- •Invisible drift compounds across retries and tool calls.
- •Recovery depends on screenshots, logs, and memory.
Replay keeps the record coherent.
- •Replay reconstructs the runtime chain from receipts.
- •GhostLog keeps continuity, posture, and checkpoints visible.
- •Recovery traces show what changed and why operators reviewed it.
Governance is continuity first: replay integrity, lineage preservation, and trust checkpoints that keep operators oriented when agent workflows cross tools, approvals, and recovery paths.
- •Lineage preservation across handoffs and retries.
- •Trust checkpoints before authority-sensitive moves.
- •Replay integrity so reviews start from the same evidence chain.
Illustrative index - not a live product metric.
Topology you can replay
Illustrative topology only: guided-demo posture, static nodes, and no live graph layout engine or customer telemetry.
Illustrative replay-safe example - not live telemetry.
Recon does not replace your runtime. It governs trust across it.
Coverage is model- and framework-neutral: connect your stack, keep receipts in GhostLog, and replay the same trust surfaces. Actual depth follows your ingestion wiring and plan limits—this table is the product contract shape, not a vendor-specific integration roster.
| Runtime | Replay | GhostLog | Trust score | Drift watch | Timeline |
|---|---|---|---|---|---|
| OpenAI | ✓ | ✓ | ✓ | ✓ | ✓ |
| Anthropic | ✓ | ✓ | ✓ | ✓ | ✓ |
| Ollama | ✓ | ✓ | ✓ | ✓ | ✓ |
| LangChain | ✓ | ✓ | ✓ | ✓ | ✓ |
| MCP | ✓ | ✓ | ✓ | ✓ | ✓ |
| Custom runtimes | ✓ | ✓ | ✓ | ✓ | ✓ |
Runtime trust protocol layer
Public pathways through the same verifier-shaped protocol surfaces the product ships: portable bundles, survivability replay, and continuity-over-time narrative. Surfaces stay illustrative and replay-safe; verifier JSON keeps carriesExecutionAuthority false.
Seeded query string (read-only demo scope): orgId=org_demo_legal&missionId=org_demo_legal:trust_graph_runtime. Public routes still respect tenant data—sign in and select a workspace to attach your own mission scope.
Governable Agent Runtime
Deploy agents inside replay-safe runtime trust infrastructure.
Governable-agent framing — replay-safe continuity in a runtime trust operating environment (protocol layer); bounded operational authority; not a marketplace or template library.
Deploy governable agents as bounded operational actors inside a replay-safe runtime trust operating environment.
Open builder narrative →Trust-state drift, recovery orchestration rehearsal, continuity memory, semantic governance, and runtime survivability — operator-shaped vocabulary with bounded operational authority.
Explore narrative →OpenAI, Claude, Gemini, and future models plug in as cognition engines while runtime trust continuity infrastructure stays the operational spine.
BYO LLM doctrine →AI-native retrieval + human-ready conversion
Clear definitions for agents; proof and drilldowns for operators
- •LangChain & agent frameworks
- •Multi-step production workflows
- •API & tool-heavy systems
When to use ReconAI
- •When your AI agent produces inconsistent or unreliable outputs
- •When multi-step workflows break without clear errors
- •When you need to validate, retry, and track agent decisions
- •When deploying agent workloads in production environments
What ReconAI does
- •Detects breakdowns in agent workflows
- •Scores outputs using a trust-based system (Reflex Score)
- •Triggers recovery actions such as retry or reprompt
- •Logs all actions for replay, validation, and auditing
How ReconAI fits in your stack
Agent stacks flow from models through frameworks into tools and APIs. ReconAI is the runtime trust operating system layer—a continuity control plane between agent execution and downstream surfaces—so evidence exports and verifier posture stay coherent without implying an autonomous operating system.
When you are mapping to common federal intake patterns or audit-grade evidence expectations, the same layer stays runtime-first: replay-evidenced traces, portable bundles, and continuity attestation—not a compliance dashboard bolt-on.
Typical stack
LLM→Agent framework→ReconAI→Tools / APIs
Built for regulated runtime environments
For teams that need AI systems to stay intake-ready, replay-evidenced, verifier-safe, and continuously admissible—without shrinking Recon into “compliance software.”
- •Intake-ready: bounded replay and GhostLog lineage operators can walk through under scrutiny
- •Replay-safe evidence and portable bundles for verifier exchange across teams or runtimes
- •Portable attestations and continuity overlays so trust stays coherent across handoffs
- •Light alignment with common federal intake shapes—honest about what the product proves at runtime
- •Continuous admissibility as ongoing runtime evidence posture — not a one-time seal or certificate substitute.
- •Portable continuity evidence plus verifier continuity across handoffs — structured echoes, carriesExecutionAuthority false.
- •Runtime intake readiness alongside trust-runtime interoperability fragments (LP-010 posture) — declarative continuity only, no wire authority.
Named ecosystems appear as interoperability context only — not sponsorship, endorsement, certification, or trademark claims; surfaces stay replay-safe and non-enforcing.
- •Designed to interoperate with LangChain- and LangGraph-style agent graphs — verifier-shaped exports remain JSON-only with carriesExecutionAuthority false.
- •Built to interoperate with OpenAI Agents-style runtime flows where continuity evidence stays replay-derived and reviewer-local.
- •Multi-agent coordination is framed through topology echoes from bundle-shaped inputs — not autonomous execution authority on external systems.
- •Portable verifier exchange posture aligns with LP-006/LP-007 storytelling — paste/parse composition beside local verifier workflows.
- •Regulated-runtime readiness uses intake-aligned vocabulary — no supervisory endorsement, government approval, or enforcement claim.
Example use case
API misfire in a multi-step agent
An agent retrieves customer data, selects an API, and executes a transaction—but picks the wrong endpoint.
The same replay-evidence path matters when autonomy must stay verifier-safe: portable bundles, continuity attestation, and bounded replay—runtime trust continuity for regulated admissibility, not checkbox tooling.
- •The error goes unnoticed
- •The workflow continues with incorrect data
- •Detects mismatch between intent and action
- •Stops or retries the step
- •Logs the failure in GhostLog
- •Assigns a Reflex Score to evaluate trust
Who should use ReconAI
- Developers building AI agents with frameworks like LangChain
- Teams deploying agent workflows in production
- Companies needing auditability and trust in AI decisions
- Platforms integrating multiple tools, APIs, and agents
- •Blind retries
- •Guessing if outputs are correct
- •No visibility into failures
- •Measurable Recon Trust Score (RTS)
- •Visible recovery loops
- •Operator-level control
Trust continuity at runtime—not another agent builder
ReconAI does not assemble prompts. It sustains runtime trust with drift signals, scoring, recovery where policy allows, and replayable lineage—portable runtime evidence operators can govern.
Drift detection
Identify when an agent deviates from expected behavior across multiple steps—before the workflow compounds the wrong state.
Reflex scoring
Score outputs on consistency, correctness, and alignment with task intent using a trust-based Reflex Score.
Policy-governed recovery
Trigger retry, reprompt, or correction loops when policy allows—after a step fails validation—without implying unattended autonomous control.
GhostLog replay
Log every action for replay, validation, and auditing—so operators can see what happened and why.
What makes ReconAI different
ReconAI does not build agents. ReconAI ensures agents behave correctly.
- •A prompt optimizer
- •A chatbot builder
- •A workflow generator
- •A trust and control layer for AI systems
- •Portable runtime evidence and trust continuity for agent actions
- •Reliability infrastructure for autonomous workflows
Most AI systems fail quietly. Recon.AI makes every failure visible—and fixable.
Multiple entry paths, one operator surface
Start from Replay Studio with the seeded guided scope, open TrustGraph runtime and continuity with the same query params, then use the quickstart hub or narrative demo when you want the install arc. Aliethia, Trust Split, missions, and batches stay available as you go deeper.
Install @reconai/sdk, run the CLI vignette, open seeded replay, then timeline / GhostLog—labeled honestly for starter vs production wiring.
Aliethia · First contact
Capture intent, establish a trust baseline (T₀), and align execution before blind automation takes over.
Mission Detail
See how entire workflows perform. Identify weak paths, low acceptance rates, and where trust breaks across a mission.
Silent policy drift · Trust Split
Long-horizon benchmark trace: watch alignment diverge—drift bands, detection windows, and how Recon intervenes before failures compound (paired runs, not a single bad output).
Batch Run Detail
Evaluate intervention impact across multiple targets. See which tasks recovered, failed, or still need attention.
No setup required • See real examples in seconds
Aliethia shows up inside onboarding when alignment matters—without leading cold technical users through a named character first. Quickstart (/start), first replay, then Recon vs Chaos when you want the narrative arc.
See autonomous policy drift over time — and how Recon detects, explains, and recovers before it compounds.
Long-running workloads quietly bend away from intent. Trust Split proves the behavior on benchmark-grade paired traces so operators get narrative clarity and technical receipts.
- •Silent drift
- •No validation
- •Failures discovered late
- •Step-level detection
- •Reflex scoring
- •Governed recovery loops
- •Full system visibility
Interactive paired run across the benchmark horizon—compare unchecked drift versus instrumented recovery.
Your TrustOps command center
Operate trust like a system. See where behavior breaks, what got repaired, and whether the intervention actually worked.
Conversion Dashboard
See trust movement across the system, spot drop-off points, and measure recovery performance.
Mission Detail
Drill into weak workflows, compare outcomes, and identify where trust leaks inside a mission.
Trust Split · policy drift
Silent operational misalignment over calendar time — benchmark-paired runs, interventions, and receipts (Vending-Bench harness underneath).
Batch Run Detail
Measure intervention impact, compare before-and-after signals, and inspect which targets recovered or failed.
Don't just detect failures. Fix them.
Recon.AI does not stop at observation. It retries intelligently, reprompts strategically, validates outcomes, and measures improvement over time.
Turn AI behavior into measurable signals
If you cannot measure trust, you cannot improve it. Recon.AI turns silent behavior into visible operational signals.
A new layer for AI systems
DevOps manages infrastructure.
MLOps manages models.
TrustOps manages behavior.
Works across your AI stack
ReconAI integrates with agent frameworks, API-based systems, and custom AI pipelines. Typical integration: wrap agent execution with the ReconAI SDK, track each step, and apply scoring and recovery automatically.
Simple explanation
ReconAI keeps runtime trust continuous in multi-step agent workflows: portable runtime evidence, Reflex-style scoring where enabled, and recovery paths that stay governable—bounded replay included.
Frequently asked questions
What is ReconAI?
ReconAI is a runtime trust continuity layer for agent systems. It preserves portable runtime evidence, supports bounded replay, and keeps multi-step behavior governable.
When should I use ReconAI?
Use ReconAI when AI agents produce inconsistent outputs, when multi-step agent workflows break silently, or when you need retry, validation, and trust scoring in production.
Does ReconAI build agents?
No. ReconAI does not build agents. It validates behavior, sustains trust continuity, preserves replayable runtime evidence, and applies recovery where policy allows—without selling “monitoring” as the category.
Start building reliable AI systems
View docs, explore use cases, and integrate ReconAI between your agents and execution layer.
No setup required • See real examples in seconds
Runtime state (illustrative)
- •Bounded replay interval pinned for reviewer compare — guided posture only; non-enforcing.
- •Verifier envelope digest echoes topology commitments — paste/parse beside local workflows.
- •Portable bundle manifest references LP-004 lineage hashes — digests and labels; no protected payloads.
{
"carriesExecutionAuthority": false,
"posture": "guided-replay-illustrative"
}Signals above are synthetic guided-demo posture — not live authority, production enforcement, or autonomous control of external systems.