Harness engineering for the enterprise
We engineer the infrastructure that wraps frontier models with tools, memory, and verification so they pursue long-horizon goals autonomously against your systems.
Our Thesis
Automations only run predefined processes. An agent — a model wrapped in a harness — interprets what you need, navigates your systems, and executes. Software runs the processes you defined. Agents handle the ones you didn't.
— BeyondFlows, Foundational Principle 01
The implementation gap
Productized agents look easy in a demo. Embedding them in an enterprise stack is a different problem — one that touches data, security, process and architecture at the same time. It takes a harness, and it takes engineers who've built one before.
Decades of context live in systems that were never designed to be queried by a model. We connect agents securely to that data — across ERPs, internal APIs, databases and custom auth — and modernize the boundary where it's needed.
Agents need entitlements, not just credentials. Right scopes, least privilege, full logging, and the ability to monitor and revert what they do. We design the control plane so agents are never a liability.
Most enterprise processes live in people's heads, in Confluence pages that nobody updates, or in spreadsheets that everyone forks. We work with your teams to extract, structure and version that knowledge until it's something an agent can actually use.
The real value comes from rethinking who does what when agents and people share a process. We redesign the hand-offs, escalations and approvals around the new collaborator — not retrofit them onto the old org chart.
End-state processes need evals: tests that verify the agent does the right thing on the cases that matter, before and after every change. We build the eval suite alongside the agent, so every model upgrade is a measured decision.
Best practices, model capabilities and architectural patterns shift faster than any enterprise can refactor. We design the system to absorb that change — and stay engaged so your stack moves with the frontier, not behind it.
Architecture
A familiar mental model: where a computer stacks CPU, RAM and OS to run apps, an agent stacks a model, its context, and a harness to run useful work.
We build our solutions on top of leading industry technologies.
Productized vs custom
Claude Cowork and ChatGPT agents work for individuals and small teams. When an enterprise needs agents embedded in its stack, wired into internal systems and running 24/7 — it needs a custom harness.
| Enterprise need | Productized harnessClaude Cowork, ChatGPT agents | BeyondFlowsEnterprise Custom Harnesses | Why it matters |
|---|---|---|---|
| Connection to internal systems | Only what vendor connectors expose | DBs, internal APIs, ERP, CRM, legacy | Real enterprises run on SAP, Salesforce, internal databases and legacy systems with custom auth. A custom harness connects directly; a productized one depends on whichever MCP servers and connectors the vendor decides to support. |
| Domain-specific business logic | Configurable, not customizable | Code-level ownership of the logic | Every enterprise has its own way to approve, escalate, validate and audit. A custom harness encodes that logic end-to-end; a productized one only gives you the hooks the vendor decided to expose. |
| Complex multi-agent workflows | Vendor sub-agent patterns | Parallel agents + deterministic steps | Enterprise processes mix AI with deterministic steps: validations, calculations, integrations, approvals. A custom harness orchestrates the full flow; a productized one stops at the vendor's patterns. |
| 24/7 operation, no human in the loop | Chat-initiated or vendor-scheduled | Cron, events, webhooks, queues | Enterprise work happens around the clock: tickets arrive, orders process, alerts fire. A custom harness triggers from your systems; a productized one waits for a user to open an app. |
| State, audit and metrics | Vendor-managed memory and logs | Your own infrastructure | Full history, embeddings, audit logs, usage metrics, per-client and per-project cost tracking — all in your own data lake, integrable with your dashboards and BI tools. |
| Compliance and data sovereignty | Vendor terms and data handling | Residency, retention, encryption, audit | Banking, healthcare, government, insurance. You control where data lives, what gets logged, how long it's retained, and produce the audit trails regulators require. |
| AI embedded in your product | Not embeddable — your customers don't use Cowork | Multi-tenant inside your SaaS | If you sell a SaaS and want to ship AI features inside your app — for your own customers — you need the API and your own harness. You're not sending your users to Cowork or ChatGPT. |
| Cost that scales with volume | Per-seat or per-task licensing | Per-token + caching and batching | At enterprise scale, per-seat or per-task licensing becomes prohibitive. Paying per token, layering in prompt caching, batching and smaller models for simple tasks lowers unit cost substantially. |
Let's build
An initial technical conversation with our engineering team to assess your agent project and propose a path forward.