Every other platform makes each employee individually faster. Evolocity makes the organization permanently better — through self-evolving harness engineering, where the agent rewrites its own executable code as it learns how your best people think.
Built by the team behind Meta’s production agent platform.
Our self-evolving harness at Meta, where nearly every engineer logs in daily.
Held state of the art for over a month, as a side project of the team.
Model accuracy and engineering throughput on Meta’s Ads ranking work.
Enterprise assistants have gotten very good at serving one person at a time. None of them turn what your organization knows into something the organization owns.
A shared assistant can recall every conversation — but not why a decision was made, what good judgment looks like, or how your best people actually think. Memory accumulates context. Institutional knowledge compounds wisdom.
Wiring the warehouse and the CRM into one shared layer still can’t answer: is this knowledge aligned with our goals? Are employees actually getting better? What capability do we lose when someone quits?
Workspace agents run tasks on a timer. They can’t notice that something looks wrong and go investigate. Institutional agency needs agents that know how your org handles an anomaly.
Generic self-improvement loops optimize for generic benchmarks. Institutional AI requires evaluation grounded in your organization’s real outcomes — the numbers your board already reads.
The harness is the layer between the model and your business. Ours modifies its own executable code through goal-driven feedback loops — so your institutional knowledge lives in a harness you own, and every behavior is inspectable by design. No black box.
Validated patterns get encoded into deterministic, committable code that sharpens over time — instead of an ever-growing context window that accumulates disorder. Skills crystallise into capability you can review in a pull request.
One agent evolves into a network of specialists, each driven by measurable sub-goals that roll up to business objectives. Generations are git tags, branches are children, worktrees are the nursery — sandboxed evaluation before promotion.
Capability tiers, an append-only audit log, per-trust rate limits, and event coordination. An agent that can rewrite itself needs a coordination protocol — that protocol is part of the product, not an afterthought.
Not everything should evolve at the same speed. Each layer of the harness has its own natural unit of change — from what the agent learns mid-session to what only moves on a training run.
What the agent learns while doing the work. Skills crystallise into committable capability code every run — the most plastic layer, changing as fast as the agent executes.
The callable surface — connectors and integrations. You add one when a new engagement needs a new external system, not every session. Scoped to the work, not the run.
Bundled packages of skills, tools, and hooks. A plugin is a versioned, distributable artifact — cut and shipped on a release cadence like any other software package.
How the agent orchestrates itself. A generation is the unit of its own evolution — git-tagged, sandbox-evaluated, then promoted. This is also where org-wide policy like security and compliance is enforced.
The slowest, most expensive, most general layer. Weights only move for a real training run — rare, costly, and worth it only for knowledge proven durable across many runs.
Bring your own model. The harness is where your advantage compounds — not the weights.
Four phases. Each one produces an asset you keep.
The agent lands with zero integrations and builds them in real time, rewriting its own connectors for your bespoke tools — the proprietary ERP, the custom LIMS, the homegrown ticketing system.
Others connect to 100 tools you don’t use. We connect to the 2–5 you actually use, in days rather than quarters.
As employees use AI across workflows, the harness captures proven patterns — what worked, what didn’t, and why. Decision patterns are logged and filtered automatically.
Every expert judgment becomes a reusable institutional asset.
The agent extracts those decision patterns and encodes them into the harness as code, skills, and memory. With high-quality skills crystallised, it shifts from reactive to proactive — flagging, suggesting, initiating.
Individual expertise becomes an institutional asset.
Humans and agents now coordinate across teams. Organizational intelligence emerges — not a set of individual skill replicas — and execution is aligned to organizational goals.
AI that grows your business, not a drawer full of individual tools.
The prevailing approach is better retrieval: index the documents, build a graph, feed richer context to a generic model, replay the patterns. It works — but the agent itself never changes.
| Dimension | Retrieval & graph platforms | Evolocity |
|---|---|---|
| How it learns | Indexes documents and activity, builds a graph, feeds context to the model | Observes the expert, then rewrites its own code, tools, and strategies |
| Where knowledge lives | An external graph database the agent reads from | Inside the agent’s executable body, committed as code |
| What improves | The context window gets richer | The agent itself gets permanently smarter |
| One agent to many specialists | No — the same generic agent with different context | Yes — demonstrated, from a single parent agent |
| Cost curve | Per seat, rising as features stack | Per organization, bring your own model — cost per outcome falls as usage grows |
AI copilots are flooding engineering orgs with code. Review capacity doesn’t scale with it. The why behind an architectural decision — the tacit judgment sitting in your senior engineers’ heads — is the bottleneck no tool captures.
Your enterprise environment — your infrastructure, your internal tools, and how your employees already use AI.
An institutional asset — a specialized managed agent platform, a skills registry, and knowledge bases that belong to you.
Per-seat pricing punishes exactly the behavior we want. Every additional person using the harness means more expert judgment captured, more compounding, and a lower cost per outcome. So we charge for the organization and you bring your own model.
One R&D team, proving the loop on a single measurable workflow.
Multiple teams, with the compounding made visible to leadership.
Org-wide, including regulated and air-gapped environments.
A joint team that took Meta’s first production-grade agent platforms from zero to one, serving tens of thousands of engineers. On top of it we shipped an agentic ML discovery engine that delivered 2× model accuracy and 5× engineering output on Ads ranking.
Our research prototype scored SOTA on SWE-bench-pro and held it at 54% for over a month against teams working the benchmark full time. We were also first to propose the meta-agent in scaffold design — the prelude to the self-evolving harness.
We work with an associate professor at Northwestern whose group does world-class research on LLMs and reinforcement learning — a strategic partnership on self-evolving research and the co-design of harness and post-training.
A pilot scopes to a single measurable workflow. If the harness doesn’t compound, you will know early — and you keep everything it built.