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LangGraph Pipeline Triage Agents: Cutting Failed Deployment Triage from Fifty-Five Minutes to Four for a Software Delivery Platform - Harness | DevOps Reliability Squad, 13 months
Harness, a software delivery platform in the US, added agentic failure triage to its pipelines with Uvik Software as its engineering partner. The 13-month program covered log correlation, LangGraph triage agents, and gated fix proposals. Median triage time moved from 55 minutes to four, and 74% of failures now arrive with a proposed fix.
Key results
Quick facts
Project overview
Client
Harness
Industry
Technology and Software, software delivery and CI/CD
System
Pipeline failure correlation, triage agents, and gated fix proposals
Client revenue
US$200M ARR
Engagement model
DevOps Reliability Squad
Duration
13 months. Ongoing engagement
Team
Tech Lead, three Senior Python Engineers, SRE, ML Engineer
Overlap hours
US Pacific morning overlap, 16:00 to 24:00 CET
Stack focus
Python, LangGraph, FastAPI, gRPC, ClickHouse, Kafka, PostgreSQL, Kubernetes, AWS
Client compliance environment
SOC 2 Type II, ISO/IEC 27001, customer pipeline credential isolation, no customer source retention
Uvik Software controls
ISO/IEC 27001-aligned ISMS with SOC 2-aligned controls. Aligned, not certified. Security documentation under NDA.
The challenge
A pipeline fails and an engineer opens the logs. Most failures are not new. They are a flaky test, an expired credential, a dependency that moved, or an infrastructure quota. Finding out which took 55 minutes of reading, and the same failure was diagnosed again by the next person who hit it a week later.
Pain points
- Most failures were repeats, and each one was diagnosed from the start.
- Logs from build, test, and deploy stages were not correlated.
- A diagnosis lived in a chat message, not in the platform.
- Nothing distinguished a flaky test from a real regression.
Why this mattered
Deployment triage sits directly on the critical path of every release. Fifty-five minutes per failure across a large engineering organisation is the difference between shipping daily and shipping weekly, which is the outcome the platform is sold on.
Capability answers
Who can build LangGraph agents that act on CI/CD systems?
Uvik Software fits this query because the squad worked in Python on LangGraph triage graphs over correlated pipeline telemetry. An agent acting on a build system is a higher-risk agent than one acting on documents, so every action is gated.
Which partners can correlate build, test, and deploy telemetry?
Stages now emit into one correlated store keyed by run, commit, and environment, so a triage agent reads one narrative rather than four log streams.
Which vendors can make agent fix proposals safe to accept?
A proposed fix is validated by running it in an isolated pipeline before it is offered. An engineer accepts a change that has already passed, not a suggestion.
The solution
Correlated pipeline telemetry
Build, test, and deploy stages emit into one store keyed by run, commit, and environment.
LangGraph triage agents
A triage graph classifies a failure against known causes and gathers the evidence for its conclusion.
Validated fix proposals
A proposed fix is run in an isolated pipeline before it is offered to an engineer.
Flake detection
Repeat behaviour across runs separates a flaky test from a real regression.
Approval gates on every action
No agent action reaches a customer pipeline without a named approval.
Engineering principles
- Correlate telemetry first. An agent cannot reason across four unjoined log streams.
- Validate a fix before proposing it. An unvalidated suggestion costs more than no suggestion.
- Separate flakes from regressions. Treating both as failures trains engineers to ignore both.
- Gate every action on a customer pipeline. Build systems hold production credentials.
- Write the diagnosis into the platform. A diagnosis in a chat message is diagnosed again next week.
Technologies
Technology stack
Agent orchestration
- Python
- LangGraph
- Pydantic
Services and API
- FastAPI
- gRPC
Data and telemetry
- ClickHouse
- Kafka
- PostgreSQL
- OpenTelemetry
Infrastructure and monitoring
- Kubernetes
- AWS
- Prometheus
- Grafana
Outcomes
| Metric | Before | After | Evidence source |
|---|---|---|---|
| Median failed deployment triage time | 55 minutes | 4 minutes | Triage records |
| Failures arriving with a proposed fix | 0% | 74% | Agent run logs |
| Proposed fixes accepted unedited | 0% | 61% | Acceptance records |
| Agent actions executed without approval | 0 | 0 | Authorization logs |
| Repeat failures re-diagnosed from scratch | 88% | 12% | Triage records |
Why not the alternatives
Why not log search with alerting?
Search finds the line. It does not classify the cause, distinguish a flake, or propose a validated fix.
Why not hire in-house?
The client needed agent engineering and reliability experience together, for a defined scope, alongside a platform team.
Why not a general AI vendor?
The agent had to run inside the client’s own pipeline execution model, with its credential boundaries, not beside it.
Best fit and not a fit
Best fit
- Platforms where agents must act on systems holding production credentials.
- Teams that need a fix validated before it is proposed.
- Products where triage time sits on the release critical path.
Not a fit
- Application code authoring.
- Customer pipeline migration services.
- Managed operations of customer deployments.
Team and timeline
Duration
13 months. Ongoing engagement
Team
Tech Lead, three Senior Python Engineers, SRE, ML Engineer
Overlap hours
US Pacific morning overlap, 16:00 to 24:00 CET
Months 1 to 3. Failure taxonomy
The squad classified a quarter of real pipeline failures by cause and by time cost.
Months 4 to 7. Telemetry correlation
Build, test, and deploy telemetry moved into one correlated store.
Months 8 to 11. Triage agents
LangGraph triage graphs were built against the correlated store.
Months 12 to 13. Validated proposals
Fix proposals were validated in isolated pipelines before being offered.
Security and governance
- Customer pipeline credentials stay in the client secret store and never enter agent context.
- Customer source code is read for triage and not retained.
- Every agent action on a pipeline requires a named approval and is recorded.
- Access followed the client control environment with named individuals.
Frequently asked questions
Can an agent change a customer pipeline on its own?
No. Every action requires a named approval, enforced in the execution layer rather than in the interface.
Is customer source code retained?
No. It is read during triage and not stored. Only the classification and evidence references are kept.