Run AI agents inside real operating boundaries

We build the platform pieces agents need before they touch business systems: controlled runtimes, model access, tool policies, state, secrets, traces, and rollback-aware workflows.

Available as a scoped platform infrastructure engagement, not a generic turnkey agent cloud.

Scope agent infrastructure View agent observability

On-request / scoped service

AI agent infrastructure is available only as a scoped platform infrastructure engagement.

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Runtime and tool boundaries

Define where agent code runs, what it can access, which tools it may call, and when humans must approve side effects.

Model gateway and cost controls

Route model calls intentionally, track token usage, set budgets, and document provider tradeoffs per workflow.

State and memory with ownership

Persist only the state each workflow needs, with retention, namespace, permission, and audit rules.

Service playbook

From problem to operating evidence

Main content is structured like a case study: context first, scoped work next, then the operating changes and evidence a team can use after handoff.

Service briefWhat we design and implementAgent runtime patternsLLM gateway and provider operationsState, memory, and retrieval

AI agents need more than a model API key. They need execution boundaries, safe tool access, state that survives retries, and infrastructure that makes failures observable. Assistance designs and implements those foundations as scoped platform work.

This service is a fit when you already have a candidate agent workflow and need to make it safe enough for a controlled pilot or production rollout.

Case-study lens

Scoped

Problem, responsibility, and handoff boundaries before implementation.

Evidence

Dashboards, runbooks, reviews, and operating records over borrowed logos.

Outcomes

Conservative summaries focused on observable operational improvement.

EvidenceSection 01

What we design and implement

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Foundation	What we deliver
Runtime model	Serverless task, container, worker, queue, or customer-cloud runtime choice with resource limits and deployment automation
Model access	Provider decision record, model gateway or adapter, rate limits, budgets, secrets handling, and environment separation
Tool execution	Tool registry, allowlists, authentication, timeouts, retries, approval gates, and audit logging
State and memory	Workflow state, vector or keyword memory, conversation retention, namespace rules, and deletion policy
Security boundaries	Network egress rules, secret injection, data classification, prompt/data redaction, and least-privilege service accounts
Operations	Traces, logs, metrics, dashboards, runbooks, incident flow, and handoff documentation

Operating modelSection 02

Agent runtime patterns

Responsibilities, response paths, and technical changes are made explicit before work starts.

Pattern	Best for	Notes
Serverless task	Short-lived enrichment, classification, extraction, or notification workflows	Good first step when tasks are bounded and external side effects are limited
Containerized worker	Longer-running workflows, custom dependencies, repository tools, or queue consumers	Gives stronger runtime control but needs patching, scaling, and logs
Customer-cloud deployment	Sensitive data, existing network dependencies, or strict ownership requirements	Keeps credentials and data in the customer's environment
Single-tenant managed runtime	Teams that want Assistance to operate the platform surface	Requires explicit scope for isolation, data residency, observability, and support boundaries

OutcomeSection 03

LLM gateway and provider operations

Expected changes are framed as practical operating improvements, not unsupported guarantees.

A gateway is useful when it reduces operational risk. It should not hide differences in model behavior.

Typical gateway responsibilities:

provider and model configuration by workflow or environment
rate limits and budget thresholds
prompt and configuration versioning
request logging with redaction rules
retries only where repeated calls are safe
cost attribution by workflow, team, and environment
provider health checks and change review

EvidenceSection 04

State, memory, and retrieval

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Agents often need state; they do not need unlimited memory. We scope persistence around the workflow.

workflow checkpoints and idempotency keys
task state for retries and resume
vector or keyword memory for approved source material
conversation history retention rules
namespace isolation by tenant, workflow, or team
deletion and export paths where required

Operating modelSection 05

Tool execution and MCP

Responsibilities, response paths, and technical changes are made explicit before work starts.

Tool access is usually the highest-risk part of an agent system. We design it before implementation.

tool inventory and risk classification
MCP server or adapter selection where useful
per-tool permissions and denied actions
read-only mode for early pilots
human approval for writes, sends, deletes, deploys, or payments
request logging with sensitive-field redaction
timeout, retry, and rate-limit policies

OutcomeSection 06

Engagement outputs

Expected changes are framed as practical operating improvements, not unsupported guarantees.

Output	Why it matters
Architecture decision record	Captures runtime, provider, state, tool, and deployment choices
Infrastructure-as-code changes	Makes the agent platform reviewable and repeatable
Tool policy matrix	Shows what the agent can and cannot do
Observability plan	Defines traces, metrics, logs, alerts, and retention
Pilot runbook	Gives operators a clear path for rollout, rollback, and support
Handoff session	Transfers ownership and known tradeoffs to your team

Next stepSection 07

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Next stepSection 08

Getting Started

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Bring one candidate agent workflow, the systems it needs to touch, and the risks you are worried about. We will map the runtime, tool, state, model, and operating boundaries before implementation. Talk to an agent infrastructure engineer →

Ready to get started?

Book a quote review or talk to an engineer.

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Pricing

Flexible scopes available. if you need custom terms or bundled service pricing.

On-request scope

Quoted