Your teams are already building agents.
The layer that governs them doesn't exist yet.
In almost every Fortune 500, engineers ship agents on the Claude Agent SDK, LangChain or CrewAI, straight onto AWS, Azure or GCP. What's missing is everything around it — who built what, where it runs, whether it's safe, whether it was tested. Lyzr is that middle layer, inside your environment.
There's a hole in the middle of your agent stack.
Frameworks on top. Cloud runtime underneath. Between them, in most enterprises, nothing — exactly where governance, safety and testing belong.
Every enterprise running agents hits the same four walls.
Not four vendors' worth of problems. One architectural gap showing up in four places — which is why point tools keep failing to close it.
No central visibility or governance
Teams deploy across multiple runtimes with no way to track which team built which agent, or which are heading to production. No promotion discipline between environments.
Nobody can answer "how many agents do we run?"No observability
Raw logs sit in a vendor console. Analysing anything means manual extracts into Splunk and hand-written queries. No agent-level traces of what an agent actually did.
Incidents get reconstructed by hand, after the factNo guardrails out of the box
Restricting which tools an agent may call — or stopping it reading its own VM config — means building and maintaining an internal module on top of whatever the cloud provides.
Your safety layer becomes a product you now maintainNo simulation framework
High-risk agents reach production without rigorous testing. Teams that start an internal evaluation framework consistently find it far harder than expected.
Production becomes the test environmentOne system, built from first principles — not five tools stitched together.
Registry, CI/CD, observability, guardrails and simulation were designed together — one control plane, not five integrations. All of it inside your sovereign boundary.
Two pieces make adoption frictionless. OpenGAP is our open protocol for defining agents. ComputerAgent lets any file-system agent framework — Claude Agent SDK, OpenClaw, Hermes, GitAgent — plug into the control plane natively. Your engineers keep working exactly as they do today.
Six things you stop building yourself.
Agent registry
A live map of every agent, owning team and environment. One page that finally answers "what do we actually run?"
Agent CI/CD
Promotion across dev, pre-prod and production, with deployment configs versioned like code. Shipping an agent stops being an act of faith.
Full observability
Every agent action traced and audit-ready — no manual log extracts, no custom queries to find out what happened at 2am.
Responsible AI guardrails
Out of the box: which tools an agent may call, what it may read, what it may never touch — down to the machine it runs on.
Simulation at scale
Up to 10,000 simulations per agent before production. The eval framework your team started building already exists, hardened.
Improvement engine + ShadowLM
Agents improve as they scale — traces feed evaluation, evaluation feeds tuning, tuning goes back through the same gates.
Four programmes already running at Fortune scale.
Retail, beauty and consumer goods. Different problems, same platform underneath.
Fortune 100 specialty fashion retailer
Governance for an agent fleet nobody could see.
Their teams built on the Claude Agent SDK and deployed to AWS Bedrock AgentCore. They liked that stack. Missing was everything around it — who builds what, where it runs, whether it's safe.
They tried a workflow orchestrator, evaluated building observability, guardrails and simulation in-house, and looked at other platforms. Every option solved one slice.
Lyzr dropped in underneath without changing how engineers work, because ComputerAgent lets any file-system framework plug in natively — and the platform runs locally, so privacy was settled on day one.
The paved road that takes any agent to production.
Agent work was happening in a dozen places — different teams, frameworks and definitions of "ready". Every launch became a bespoke security and compliance conversation.
The Lyzr control plane became the single path: register, inherit a deployment config and guardrail policy, pass a simulation gate, carry a named owner and audit trail into production.
Trend to published content, at social speed.
Short-form is a volume game. The bottleneck was never the idea — it was the distance between spotting a trend and having brand-safe assets live.
We run that distance as an agent pipeline: signals in, brief generated, a full variant set per platform, every asset checked against brand and claims guardrails before scheduling. Performance feeds the next brief.
The same guardrail and simulation machinery governs content agents. Creative gets velocity; legal and brand keep the veto.
Catalog onboarding that keeps up with the buyers.
New product data arrives incomplete, inconsistent, and faster than merchandising can process it. Every hour a SKU sits in a queue is an hour it isn't selling.
Enrichment agents extract attributes, map the taxonomy, generate titles and descriptions, and validate images and specs — each output scored for confidence. High-confidence items publish automatically; the rest arrive in a ranked review queue with the uncertainty flagged.
Copy is written to be retrievable — by traditional search and by the answer engines now sitting between shopper and product page.
Off-price runs on speed and judgement. Agents are useful exactly where those two meet.
Opportunistic buying means the assortment never sits still and the data never arrives clean, across a thousand-plus stores. Six places we'd start — each an agent, all under one control plane.
Closeout and vendor intake
Packs arrive with whatever data the vendor sent. Enrichment agents normalise attributes, map your taxonomy, write shelf-ready copy and score their own confidence.
Allocation and size curves
Agents propose store-by-store allocation against sell-through, climate and local mix — with the reasoning written down, so a planner can accept, adjust or overrule.
Markdown and clearance timing
An agent watches sell-through by store and recommends markdowns inside your pricing policy. Guardrails hold the floor: it can propose, never exceed the rules.
Store operations copilot
One place to ask about policy, scheduling, planograms, returns and safety — grounded in your documents, every answer traced.
Social and content velocity
The trend-to-content pipeline running at a global beauty group: signals in, brand-checked short-form variants out, performance fed into the next brief.
Service and returns
An agent with real order, inventory and policy context handles routine volume, escalates cleanly, and never invents a policy — the guardrails won't let it.
Three roads out of this. Two of them you maintain forever.
Every enterprise we meet has considered the first two. The pattern is consistent: each solves one slice and leaves the rest to build and integrate.
| Build it in-house | Point tools, stitched | Workflow orchestrator | Lyzr | |
|---|---|---|---|---|
| Agent registry across teams | Someone's spreadsheet | Per-tool, not central | Not its job | Native |
| Promotion across dev → prod | Bent out of app CI | Partial | Workflow-level only | Agent CI/CD |
| Agent-level traces | Manual log extracts | You write the queries | Step-level, not agent-level | Audit-ready |
| Tool + environment guardrails | An internal module to maintain | Content filters only | Out of scope | Out of the box |
| Pre-production simulation | Harder than expected | Separate eval vendor | None | 10,000 runs per agent |
| Keeps existing frameworks | Yes | Some | Rewrite to its model | Any, via ComputerAgent |
| Runs inside your boundary | Yes | Mostly SaaS | Varies | Fully sovereign |
| Who maintains it in year three | Your platform team | Your platform team | Your platform team | Lyzr |
The platform runs inside your environment. Not ours.
This is why security review stops being the long pole. There is no "trust us with your data" conversation, because the data never leaves.
Ninety days from first conversation to a governed fleet.
No rip-and-replace. The first thing we do is inventory what you're already running.
See what you already have
ComputerAgent points at the agents your teams already run. The registry populates itself, and you get the first honest count of the fleet.
One agent, end to end
One high-value workflow — catalog intake is the usual pick — through the full lifecycle: build, simulate, gate, promote, trace.
Guardrails across the fleet
Tool and environment policies across the registry. Traces flowing. An audit trail your risk function can read unaided.
Teams onboard themselves
Second and third workbenches go live. The platform is now the default path, and adoption stops needing a programme manager.
Start with a working session, not a demo.
Ninety minutes with your AI enablement and platform leads. We map the agents you run, find which of the four walls you're hitting, and show the control plane against your stack.
What you'll walk away with
- A count of the agents running across your teams — usually the first anyone has.
- A written read on where visibility, observability, guardrails and testing are weakest for you.
- A shortlist of two workflows worth governing first, with the reasoning.
- A deployment shape that fits your cloud and security review, not a generic slide.