From ticket to reviewed pull request
Four agents pick up requirements, write the code, prove it against real tests in an isolated sandbox, and review it — inside your existing Jira, Azure DevOps, and Git workflow, under a human approval gate.
Every ticket,
fully briefed and PR-ready
TICKET-142
Brief Confidence
High
6 signals aligned
Sandbox Tests
47
Passing · PR open
Review Rounds
2 / 3
Awaiting human
Response Time
12m
End to end
Ticket
TICKET-142
Category
Auth Flow
Budget Signal
Confirmed
Sandbox
Docker · ephemeral
Priority
Immediate
Owner
Dev · A1
Context Signal Breakdown
Domain Brief
Grounded domain brief assembled from Jira ticket and codebase structure. Every fact tagged with confidence — verified facts dominate.
Sandbox Test Results
Last run
47 / 47 tests passing · container destroyed
Tests authored alongside code, run in an isolated Docker container against synthetic fixtures.
Next Steps
- 1Human reviews PR and approves or requests changes
- 2Merge to main after human approval — never automated
- 3Curated context committed alongside the code change
Remove the friction
from every agent handoff.
The Agent Pipeline acts as a tireless engineering co-pilot that never forgets the checklist. It grounds every run in a curated brief, proves the code in an isolated sandbox, and prepares a reviewed PR — so your team ships faster without sacrificing quality or governance.
The realities of unmanaged context at scale.
- Input and re-sent context tokens dominate the bill — not generated code
- Unconstrained coding agents vary up to 30x on identical tasks
- Variance tracks with retrieved context quality, not the underlying model
- Existing codebases get poorly-served without structural grounding
An engineering intelligence engine
built for real-world dev teams.
The Agent Pipeline translates dense ticket requirements into clear, prioritized actions. No generic AI walls of text — just the specific briefs, sandbox results, and review steps your team needs to move code forward.
Feature 01
Context that compounds
Every verified fact is committed back to the shared context graph. Each PR makes the next one start from a better position — no re-guessing the same domain rules.
Feature 02
Agent handoff, not hand-cuff
Agents pass structured state between stages — brief, code, tests, review — so nothing is re-derived from scratch and the pipeline stays portable.
Feature 03
Faster rounds, same gate
Automated review rounds compress from hours to minutes. The human gate stays exactly where it is — at the end, with final authority over every merge.
Built on proven,
enterprise-grade technology
Core
Agent
Pipeline
Enterprise-grade
Frontend
React
Backend
Python · FastAPI
Agents
Model-agnostic adapter layer
Orchestration
Ports & adapters
Identity
Per-developer keys
Sandbox
Docker · ephemeral
An intelligent
engineering pipeline for every ticket.
The Agent Pipeline wraps your existing engineering motion with an AI layer. No new rituals. No duplicate spreadsheets. Just smarter briefing, code generation, and review coordination from the moment a ticket arrives.
Capture & brief automatically
Requirements enter from Jira or Azure DevOps. The Context Agent assembles a grounded domain brief and tags every fact verified, inferred, unknown, or stale — before any code is written.
Build & verify in isolation
The Coder Agent writes code and tests under the developer's own identity. The Sandbox runs them in an ephemeral Docker container against synthetic data. Failures loop back; only passing PRs open.
Review, gate & commit to git
The Reviewer Agent runs up to 3 automated rounds. Then a human reviews and merges — always. The curated context is committed alongside the code so the next PR starts smarter.
Dropping into a codebase it didn't write —
without guessing.
Most coding agents perform well on projects they started and poorly on an existing one. That gap is exactly where wrong implementations and wasted retries come from. The Context Agent closes it by tagging every fact with how sure it actually is.
Confidence tags
04 tagsFour states every fact can hold — expand any tag to see what it covers.
Context rules
EnforcedHow context moves from inferred to verified — the four governing rules.
Structural facts — what calls what, what imports what — are extracted directly from the codebase and can be trusted immediately; business rules and constraints come from existing docs or a short one-time Q&A with someone who knows the system.
A fact only moves from inferred to verified with independent evidence — a graph edge, a passing test, or a human sign-off in the same review loop already used today. The model raising its own confidence is never enough on its own.
This curated context is committed to git alongside the code change, so every developer's agent works from the same shared understanding instead of each one quietly re-guessing the same facts.
Designed to degrade gracefully — if any single layer is unavailable, the Coder Agent keeps working with what it has, rather than stalling.
Built to sit inside your stack —
not replace it.
Every integration is an adapter. The pipeline has no opinions about your model, your orchestrator, or your identity provider beyond what's needed to ship code safely.
Model-agnostic
Agent logic talks to a model through an adapter, not a hardwired call — the underlying model can change without rewriting the agents.
Orchestration-portable
The same ports-and-adapters approach means the pipeline isn't locked into one orchestration platform. LangGraph is supported as an optional adapter, not a dependency.
Individual developer identity
Each developer connects their own Anthropic API key and Git token. Every commit and PR is attributable to a real person, not a shared bot account.
Requirement-source integration
Connects directly to Jira and Azure DevOps for requirements intake — the pipeline starts where your team already tracks work.
The next generation of agentic delivery —
already in the pipeline.
This isn't a static system. Every pipeline run feeds back into the shared context, sharpening the domain brief for the next developer. The result compounds: fewer wrong implementations, less context re-guessing, and a codebase that gets easier to work in over time.
Context that compounds
Every verified fact is committed back to the shared context. Each PR makes the next one start from a better position.
Agent handoff, not hand-cuff
Agents pass structured state between stages — brief, code, tests, review — so nothing is re-derived from scratch.
Faster rounds, same gate
Automated rounds compress from hours to minutes. The human gate stays exactly where it is — at the end, with final authority.
Turn pipeline quality
into insight leadership can act on.
Pipeline snapshot — last 30 days
Context cost reduction
30×
vs unconstrained agents
Context token share of spend
>99%
addressed by briefing
Automated review rounds
3
before human gate
Merge on agent alone
0%
human required · always
Every PR sees a human. Period.
Whether the Reviewer approves on round one or hits the cap on round three — every pull request goes to a person before merge.
Nothing merges on agent approval alone. The three-round cap between the Coder and Reviewer agents limits how many automated passes a PR gets before it reaches a person — it isn't the trigger for human involvement.
Leadership views you can act on
Four views give leadership a full read on pipeline quality — from overview down to churn patterns.
Pipeline views
04 viewsFour perspectives on pipeline quality — expand any view to see what it covers.
Agent throughput
LiveTop-performing pipeline stages this month, ranked by qualified throughput.
94 briefs assembled
91 PRs opened
89 rounds completed
One ticket, end to end.
From the moment a requirement lands on your board to the moment a human merges the PR.
T + 0Requirement enters the pipeline
A ticket in Jira or a work item in Azure DevOps is picked up by the Context Agent. No prompt is hand-written — the requirement is read directly from the source.
T + 1Domain brief is assembled
Structural facts are extracted from the codebase. Business rules come from existing docs or a short Q&A. Every fact is tagged verified, inferred, unknown, or stale.
T + 2Coder Agent writes code + tests
Under the developer's own API key and Git identity. Commits carry their name. The tests are written alongside the implementation in the same pass.
T + 3Sandbox runs the tests
Isolated Docker container, synthetic data only. If tests fail, the agent fixes the code and re-runs. The PR isn't opened until they pass — then the container is destroyed.
T + 4Reviewer Agent does up to 3 rounds
Automated review passes. The cap exists to bound churn, not to replace the human — the PR is queued for human review in parallel.
T + 5Human decides
Fix directly in the branch, or merge. Nothing merges on agent approval alone. This is the only step in the pipeline with final authority.
Turn engineering discipline into
a system your team can scale.
Fewer wasted agent tokens
Ground every run in a curated brief so the agent stops re-guessing the same facts on every PR.
Live pipeline visibility
Track briefs, sandbox runs, review rounds, and merge gates — all from one workspace.
Positive operating rhythm
Reward fast, high-quality reviews and consistent context curation across the team.
What Our Customers Say
Trusted by IT professionals and security teams at companies of all sizes.
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CTO at TechCorp
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CEO at InnovateCo
Ready to accelerate your engineering pipeline?
See how the Agent Pipeline helps your team brief faster, ship smarter, and merge with confidence — under a human gate that never moves.