Build an agentic software factory
Agents that carry real delivery work, on a shared record of every decision your team has already made.
Fabric connects Jira, GitHub, Slack, meeting notes and company documents into persistent, searchable context. Product managers and engineers work from the same record. Agents use that context to research a question, draft a specification, inspect the codebase and carry repeatable delivery work, with sources attached and people making the calls. Our own delivery teams work inside Fabric every day.
Built for product and engineering teams whose decisions, customer context and delivery work live in different systems.
Your agents keep starting from nothing
Two things decide whether an agent is useful: what it knows about your product, and whether anyone can check what it did. When neither is in place, it shows up in three ways.
Every session relearns the product
An agent with a context window forgets the decision it was told about last week. It reintroduces a customer who has been on the account for two years, and it argues for an approach the team rejected in March.
The output is plausible and wrong
Nothing in the answer points anywhere. A specification reads well, nobody can trace which document it came from, and checking it takes longer than writing it would have.
Nobody can say what changed
The work happened. Which agent did it, what it was given, and who approved it are spread across a chat log, a ticket and somebody's memory of a call.
All three come from the same gap. The context lives in five systems and the agents live outside all of them, so every run begins by reconstructing what the team already knew.
The shift
The work changed. The way it gets made did not.
Three routes from an ask to shipped software. One of them was designed around agents.
Written by people, remembered by people
Traceable, and slow
Agents with no memory
Fast, and unaccountable
The factory
Fast, traceable, and it keeps what it learned
What gets installed
Six parts, running in your environment. You can adopt them separately and most teams do, starting with the context store.
A context store with a memory
Jira, GitHub, Slack, meeting notes and company documents in one searchable record that persists between sessions. The decision from March is still there in September.
Retrieval that cites
Hybrid search, and every answer comes back attached to the passage behind it. Checking an answer means reading the source rather than trusting the summary.
Fifty agents with defined jobs
Not one assistant asked to do everything. Research, specification drafting, codebase inspection and security review are separate agents with separate scopes.
A hundred integrations over MCP
Agents reach the systems your team already uses, through the Model Context Protocol, under permissions somebody granted rather than credentials somebody pasted.
A durable runtime underneath
An agent run that takes hours and waits on a person survives a restart and a deploy. The run resumes where it stopped instead of starting again.
The trail, as a property of the system
What an agent was asked, what it read, what it did and who approved it are recorded because the runtime records them, not because somebody remembered to log it.
10 → 3
People on delivery work that used to take ten
We ran it on ourselves first
Fabric was not built to sell. It was built because our own delivery work did not scale, and our teams have used it every day since. The starter kits, the components, the prompts and the decisions of a decade are what it reads from, which is why it is worth anything at all.
For product teams
Keep the product decision attached to the work.
A customer call changes the scope. The reason can get lost between meeting notes, the roadmap, Jira and the pull request. Fabric keeps that chain connected, so the team can see what changed, why it changed and what the decision affected.
- 01
Find the answer
Search meetings, documents, Slack, Jira and GitHub together. Each answer points back to its source.
- 02
Turn decisions into work
Carry an approved decision into a plan, specification or ticket without losing the conversation behind it.
- 03
Build with the full context
Give engineers and agents the same requirements, technical decisions and codebase history before work begins.
- 04
Take it to production
Keep approvals, implementation history and the reason for a change connected to the artifact that shipped.
What is in it
- Agent orchestration
- Deep research
- Hybrid search and RAG
- Codebase and security agents
- MCP integrations
- SDLC automation
What changes for the team
Product knowledge becomes part of delivery.
Fewer context resets
Product, design and engineering stop reconstructing the same decision in separate tools.
Work that can be traced
A specification, ticket or code change can point back to the source and decision that created it.
Agents with useful context
Agents work from the team's documents, code and delivery history instead of starting with an empty prompt.
The system underneath
Fabric works as one system.
Our delivery teams use Platform, Harness and Experiments together. Governance surrounds the work, agents carry repeatable tasks, and evaluations catch regressions before production.
TechFabric Platform
Routes each state change through the same policies, state machines and audit trail.
TechFabric Harness
Runs durable agents with approvals, governed tools, model routing and sandboxed execution.
TechFabric Experiments
Measures agent and model changes against owned data, with gates that stop a regression from shipping.
The work this was built out of.
Fabric exists because the same problems arrived often enough to be worth solving once. These are the engagements where we learned them.