AI Agents
Autonomous AI engineers that ship code
Give an agent a PRD; it returns a tested pull request. Prodedify's agents plan, build, test, and ship — coordinated by a multi-agent router and grounded in your product knowledge graph.
An AI agent isn't a chatbot. It's an autonomous engineer that reads a requirement, plans the work, writes and tests the code, and submits a pull request you can review. In Prodedify, every run happens in an isolated cloud sandbox, coordinated by a multi-agent router and grounded in your product knowledge graph — so the AI does the work, not just the talking.
Multi-agent orchestration
A router coordinates specialized agents — a planner, a builder, a tester, a reviewer — across a single piece of work, so each step uses the right tool for the job instead of one generalist trying to do everything.
- A planner decomposes a PRD into buildable tasks with dependencies
- A builder writes the code; a tester runs it; a reviewer checks for regressions
- Handoffs between agents are explicit and recorded in the run log
PRD → pull request, end to end
An assistant suggests. An autonomous engineer ships. Give an agent a product requirement, and it returns a pull request your team can review — with the work decomposed, the code written, the tests run, and the change explained.
- Decompose a PRD into a structured plan you can inspect before work starts
- Implement, test, and iterate inside a per-run cloud sandbox
- Open a pull request with a clear summary, diff, and test results
Sandboxed execution by default
Letting AI write and run code only works if the blast radius is contained. Every Prodedify agent run executes inside an isolated cloud sandbox — strong process and network isolation between the agent and the rest of your stack.
- Per-run Firecracker microVMs via E2B — fast boot, hard isolation
- Restricted outbound network by default; explicit allow-listing
- Resource limits and full audit trail of every action the agent took
Grounded in your product knowledge graph
Agents that lose context drift. Prodedify keeps every requirement, decision, and shipped change connected in a live product knowledge graph, so agents and teammates are always working from the same source of truth.
- Requirements, decisions, code, and outcomes connected as one graph
- Agents query the graph for context before they plan and build
- Context survives across runs, repos, and team members
Real tool use via MCP
Code is only part of the job. Agents in Prodedify reach the rest of your stack — databases, search, internal APIs, design files — through the Model Context Protocol, an open standard for AI tool use.
- MCP-native: any MCP server is one configuration away
- Bring your own MCP servers for proprietary internal systems
- Every external call is auditable in the agent run log
Human-in-the-loop review
The human always has the final say. Agent work shows up where your team already reviews it — as a pull request with a clear summary, a diff you can read, and a trail you can audit.
- Nothing reaches your main branch without a PR you approve
- Every action the agent took is visible in the run log
- Reject, re-prompt, or hand off to a teammate at any point
AI agents, answered
An assistant suggests; an autonomous engineer ships. Most tools summarize tickets or draft text. Prodedify's agents plan, write, test, and open pull requests end to end — with multi-agent orchestration and a knowledge graph underneath, so the AI does the work, not just the talking.
Prodedify uses leading frontier models from Anthropic and OpenAI under the hood, with the router selecting the right model for each step (planning, code generation, review). Model choice is an implementation detail of the platform — your team works with agents, not a specific model.
Yes. Every run produces a complete activity log — the plan it generated, the files it touched, the commands it ran, and the test results — alongside the pull request. Reviewers see both the diff and the reasoning.
Agents never write to your main branch directly. They run inside isolated cloud sandboxes with restricted network access, produce a pull request, and wait for human approval. Network and tool permissions can be scoped per project and per role.
Yes. Agents reach external systems through the Model Context Protocol — an open standard for AI tool use. Any MCP server (a database, a search index, an internal API) is one configuration away, and you can run your own MCP servers for proprietary tooling.
Yes. Agents can be scoped to a single repository for safety, or coordinated across multiple repos when a feature genuinely spans them — with the same human-in-the-loop review and audit trail in every repo they touch.
Explore the platform
Put agents to work
Start free, give an agent a PRD, and see what it returns. Bring your team when you're ready.