Your AI agents are doing more than generating responses.
Rethen connects agent activity across models, tools, workflows, and external systems so your team can follow the work and understand what happened next.
Infrastructure for the next generation of autonomous software.
Agent Run #8421
10:01:24 · 18 LLM calls · 7 tool calls
18 LLM calls
GPT-4o, Claude 3.5 · 82k tokens
7 tool calls
GitHub, Jira, Search
4 systems touched
GitHub · Jira · CI/CD · Deploy API
GitHub PR #482
Merged 10:13:08 · 3 executions
Deployment #193
api-gateway v2.1.4 · 10:18:47
Production Incident #91
42 connected events · 3 executions
hover or click nodes to inspect
Market context
Agents are moving from answering questions to doing work.
21%
have mature governance
of surveyed organizations report mature governance for autonomous AI agents
Deloitte State of AI, 2026More agents deployed means more autonomous work, more systems touched, and more outcomes to account for. The question of what happened is getting harder to answer.
The problem
The trace ends before the story does.
What execution tracing shows
Agent
LLM
Tool
Tool
Response
The trace captures what the agent did during execution.
What actually happened
Agent
LLM calls
Tools
GitHub
PR #482
CI pipeline
Deployment #193
Production
Incident #91
Production systems need you to understand what came after.
Questions teams are left with
Q01
Which AI executions were connected to this failed deployment?
Q02
What did this agent change before this incident appeared?
Q03
Which tools and systems did this workflow touch?
Q04
What happened after this agent completed?
Q05
Can we reconstruct the chain later?
Q06
Who or what triggered this sequence of events?
How Rethen fits
Observability shows the execution.
Rethen follows the work.
AI Observability
Agent
LLM
Tool
Response
Traces and logs of what happened inside the agent execution. Spans, tokens, latency, tool calls.
Rethen
Agent
Execution
Tools
External systems
Entities
Outcomes
Connects execution data with the systems, entities, and outcomes around it.
Rethen is not trying to replace your existing tracing stack. It connects execution data with the systems and outcomes around it — so you can move from “the trace ended” to “this is what happened next.”
How it works
Connect the pieces your existing systems keep separate.
Collect
Bring together activity from LLMs, agents, tools, OpenTelemetry, GitHub, Jira, deployments, and webhooks.
Resolve
Rethen builds relationships between events and entities across systems that use different identifiers.
Agent Run #8421
PR #482
Deployment #193
Connect
One piece of AI work can connect to many events, and one outcome can connect back to multiple executions.
3 agent executions
1 PR
1 deployment
1 incident
Investigate
Start from wherever the question begins and follow the chain — forward from execution or backward from outcome.
Execution
PR
Deployment
Incident
Product interface
Start anywhere. Follow the chain.
An incident surfaces at 11:42 AM. Rethen traces it back through the deployment, the PR, and the agent run that created it.
Incident #91 · Production deployment failure
11:42 AM · Detected automatically · P1
Connected activity
Agent Run #8421
Started 10:01:24
GitHub PR #482
Merged 10:13:08
Deployment #193
api-gateway v2.1.4
Production Incident #91
11:42 AM · active
click a node to inspect
Bidirectional
Investigate from the AI or from the outcome.
Production questions rarely start in the same place. Rethen lets teams move through the chain in either direction.
Infrastructure
The graph underneath the activity.
Rethen treats agent activity as a connected system of events and entities — not isolated logs.
Entity relationship model
Entities
Relationships
Every event in Rethen is typed and linked. An agent run doesn't just exist — it executed, called tools, and created artifacts that triggered downstream events.
The graph model is what makes historical replay and bidirectional investigation possible — relationships are explicit, not inferred at query time.
Historical replay
Your investigation should not depend on when you noticed the problem.
Rethen keeps raw activity separate from derived relationships, allowing historical analysis to be recalculated as your understanding changes.
How replay works
Raw events
Immutable activity log — never rewritten
Relationship rules
Versioned, updatable attribution logic
Attribution
Derived graph — recalculated on rule change
Rule version comparison
+35 additional outcomes attributed by refining the rule
Raw event storage is separate from the derived relationship graph. When attribution rules improve, the graph is recalculated against the full historical dataset — no data is lost and no manual backfilling is required.
What you get
Know what your agents actually did.
Follow AI work across systems.
Trace agent activity past the execution boundary and into GitHub, CI/CD, deployments, and other external systems.
Investigate incidents faster.
Start from an alert or incident and navigate directly to the agent executions and tool calls that preceded it.
Connect AI activity with real outcomes.
Link autonomous work to engineering and business entities — PRs, deployments, tickets, incidents — not just trace spans.
Keep the history you'll need later.
Raw events are stored separately and durably. Investigations can happen hours, days, or weeks after the fact.
Recalculate when rules change.
Attribution rules are versioned. When your understanding of the system improves, replay the full history against updated logic.
Build on your existing stack.
Rethen connects to what you already have — OpenTelemetry, GitHub, Jira, deployment pipelines — without replacing them.
Use cases
Built for teams deploying agents into real systems.
Engineering
Understand what AI changed before production changed.
When a deployment breaks production, reconstruct every agent execution that contributed — from model calls to the PR that triggered the deploy.
Activity chain
Agent
PR #482
CI pipeline
Deployment #193
Incident #91
Integrations
Built to sit across your existing stack.
Rethen connects to the tools you already use. Below is an honest view of what works today and what is on the roadmap.
Available
OpenAI
LLM
Anthropic
LLM
OpenTelemetry
Tracing
GitHub
VCS
Jira
Tracking
Webhooks
Generic
Planned
Google Gemini
LLM
GitLab
VCS
Linear
Tracking
PagerDuty
Incidents
Datadog
Monitoring
AWS CDK
Deploy
Audience
For teams putting agents into real systems.
AI & ML Engineers
Understand agent executions beyond model traces. See what systems your agents touched and what changed.
Platform Engineers
Create a common observability layer across agents, infrastructure, and the external systems they touch.
Engineering Leaders
Investigate AI-related production events without manually stitching together logs from five different systems.
AI-First Companies
Build a historical record of autonomous software activity that scales as your deployments grow.
Early stage
We are early.
Rethen is being built around a problem we believe will become more important as autonomous software moves deeper into production.
We are currently building the infrastructure, testing the model with real workflows, and learning where teams feel the problem most strongly.
Talk to us about how you're tracking agent activity→Where we are today
Core event collection infrastructure
In progress
Relationship resolution engine
In progress
GitHub & OpenTelemetry integration
In progress
Investigation interface
Prototype
Historical replay & backfill
Planned
Attribution rule versioning
Planned
Multi-tenant deployment
Planned
Early access
Know what your agents did.
Follow the work. Understand the outcome.
Early access to the investigation interface
Ability to connect your agent workflows
Direct conversations with the team building Rethen