Production-grade AI agents.
Deterministic systems integration.
Beyond prompt engineering and demo wrappers. We build resilient, tool-calling agent systems with typed schemas, strict permission boundaries, and direct integration into enterprise commerce stacks.
The Engineering Gap
Why production agents require systems engineering
Most AI demos collapse when exposed to messy enterprise APIs, rate limits, and edge-case exceptions.
The Brittle Prototype Trap
- • Unconstrained Prompts: Relying on language models to figure out complex tool parameters without validation schemas leads to frequent runtime schema mismatches and tool call failures.
- • Loop Death Spirals: When an API fails, naive agents loop indefinitely, generating thousands of dollars in wasted inference tokens.
- • No Concurrency Control: Simultaneous agent processes writing to the same catalog or customer records without transaction locking.
Buildeon Production Architecture
- • Deterministic State Machines: Workflows governed by explicit state transitions, cyclic recovery paths, and hard timeout budgets.
- • Strict Schema Sandboxes: Every tool argument is verified through typed validation (Zod/Pydantic) before any external API is touched.
- • Transactional Locks & Rollbacks: Database and inventory state mutations are isolated, idempotent, and reversible upon error.
Capabilities & Stack
Four engineering pillars of our agent practice
How we design, evaluate, and operate production agent systems at scale.
1. Multi-Agent Orchestration & State Machines
We decompose complex objectives into specialized subagents—a Router Agent, a Catalog Specialist, a Logistics Resolver, and a Verification Guard. Agents communicate through typed events, maintaining clear context windows and eliminating cognitive overload.
2. Tool-Calling Middleware & Commerce Connectors
We engineer custom tool-calling adapters for your existing REST, GraphQL, and Webhook APIs. Whether orchestrating inventory lookups in Akeneo, order status checks in Shopify Plus, or invoice drafting in Odoo, our middleware handles authentication, rate-limiting, and error transformation.
3. Continuous Evaluation & Drift Observability
Every production agent is benchmarked against a golden evaluation suite before deployment. In production, we log full execution trajectories (inputs, token costs, reasoning steps, tool outputs) to detect model drift, hallucination patterns, and latency degradation in real time.
4. Human-in-the-Loop & Security Sandboxing
We implement defense-in-depth security: least-privilege tool tokens, prompt injection filtering, and automated human-approval escalation queues for sensitive operations. You maintain 100% control over the financial and reputational boundaries of your agents.
Own your agent infrastructure. Cut recurring seat taxes.
We leverage self-hostable tools and open architectures—including n8n, OpenClaw, Hermes Agent, and private VPS deployments—enabling you to run custom workflows without paying $50/user monthly software taxes.
Delivery Process
From technical audit to production deployment
A rigorous, milestone-driven delivery cycle designed to mitigate technical and operational risk.
System & API Audit
Inspect current APIs, data schemas, rate limits, and latency budgets to establish technical feasibility.
State Spec & Sandbox
Define typed tool schemas, state transitions, human approval triggers, and golden evaluation benchmarks.
Staging & Penetration
Simulate edge-case failures, prompt injection attacks, and concurrency loads in isolated test environments.
Production & Tracing
Live deployment with continuous token monitoring, trajectory logging, and SLA-backed maintenance.
Engineering FAQs
Questions about our AI agent development practice
Why do prototype AI agents fail when deployed to production?
Prototypes usually rely on unconstrained system prompts and raw LLM tool selection. In real production, this causes cascading failure modes: unhandled API exceptions, schema drift, rate-limit throttling, runaway inference costs, and destructive unauthorized mutations. Production systems require deterministic state machines, typed payload validation, and sandboxed execution boundaries.
How do you keep agent token and inference costs predictable?
We architect agents using tiered model routing—dispatching cheap, deterministic tasks to smaller fine-tuned models or self-hosted weights (such as Llama 3 on private VPS), reserving frontier reasoning models only for high-complexity steps. Additionally, semantic caching and prompt compression drastically limit redundant context token consumption.
Can agents run entirely on our private infrastructure?
Yes. Where data residency or proprietary catalog privacy is mandatory, we deploy agents using self-hosted runtimes (e.g. OpenClaw, Hermes Agent, n8n) connected to local or dedicated private model instances, ensuring zero customer PII leaves your virtual private cloud (VPC).
How are human approvals integrated into automated agent workflows?
We implement asynchronous approval queues. When an agent computes an action flagged as consequential (such as issuing refunds, modifying pricing tiers, or bulk publishing updates), the state machine halts execution, posts a structured summary card to your Slack, email, or dashboard, and resumes only upon cryptographic token approval.
Architect production AI agents for your business
Schedule a technical architecture call with our senior engineering team to discuss your system integration, data requirements, and agent workflows.