Agent infrastructure for B2B operations

Deploy AI agents that quote, reserve, and transact across your B2B stack.

IdeaBosque connects AI agents to ERP, CRM, ecommerce, supplier catalogs, booking, payments, and support knowledge through governed MCP modules. Buyers get accurate, reservable quotes in minutes, while every tool call remains tested, logged, and production-ready.

Request a scoped buildRead the engineering brief

One-week discovery. You get a system inventory, workflow map, and fixed scope whether or not you build with us.

First agent live in 5-8 weeksEvery tool call loggedOwn the code or let us run itFrontier or open-weight models
Governed transaction flow
BuyerStructured request intake
AgentQuote, hold, approve, hand off
MCPGoverned modules for each backend
SystemsERP, CRM, ecommerce, payments, graph
Rate-limited toolsAudit logsCode handover
01—System Map

One backbone. Every backend wired through the same module pattern.

No ad-hoc glue. RFQ engines, supplier catalogs, CRM, ERP, ecommerce, booking, payments, and knowledge graphs are each wrapped in MCP modules with consistent tools, audit logging, and rate limits.

AI agent orchestration is the coordination layer that routes an agent's intent across many backends. IdeaBosque's orchestration backbone does this through governed Model Context Protocol (MCP) modules, so every tool call is tested, rate-limited, and logged.

02—Capabilities

Five production systems AI vendors demo but rarely ship.

01

AI Agent Orchestration Backbone

Stop stitching point-to-point integrations that break when one system changes. A multi-step agent runtime that routes intent across MCP modules, manages workflow state, and enforces rate limits, structured errors, and audit logs from the start. Conforms to the IdeaBosque MCP module code standard.

02

System Connectors as MCP Modules

Each connector is built once, tested, and reused — not rebuilt per project. Reusable, code-reviewed MCP modules — MCP servers that expose governed tools, resources, and prompts — for HubSpot, NetSuite, BigCommerce, WooCommerce, Shopify, Brightpearl, ShipStation, Canto, ResolvePay, and more, each tested, rate-limited, and Python 3.8+ compatible. Each module is tested, rate-limited, and audited — the governance layer that 200,000 community MCP servers lack, per the OX Security disclosure and OWASP MCP Top 10.

03

Knowledge Graph Reasoning

Answer the questions your database can't: substitutes, alternatives, and compatibility. Neo4j-backed product, catalog, supplier, customer, and industry-knowledge graphs for questions a relational schema cannot answer cleanly: alternate suppliers, substitute parts, margin rules, lead-time matches, industry taxonomies, and cross-reference standards. The same graph also powers customer support — surfacing context for inquiries, resolving tier-1 questions, and reducing escalation rates.

04

Data Pipelines

Your warehouse stays current without a data engineer babysitting loads. Dagster-orchestrated pipelines that move source-of-truth data into S3, Redshift, and Athena with Hive-partitioned files, watermark-based incremental loads, and idempotent writes.

05

AI Agent RFQ-to-B2B Workflows

Agents that execute, not just advise: RFQ intake, catalog discovery, supplier quote generation, tiered pricing, availability holds, FX, cancellation snapshots, RMA processing, asset sync, and B2B order intake — each action posted to the system of record with human approval, not summarized in a chat window. Delivered on our platform, with code ownership and private deployment available.

→

See the RFQ architecture

Detailed entity model, workflow states, and the system-of-record integration pattern for request-for-quote automation.

03—Engineering Standards

Every connector looks the same — on purpose.

“Every connector we ship looks the same.”

  • Uniform MCP_CONFIGURATION pattern — tools, resources, prompts declared the same way in every module
  • Python 3.8+ compatibility — modules run on legacy infra without runtime upgrades
  • httpx HTTP/2 clients with exponential backoff and pluggable rate limiters
  • Audit-first: every tool call logs request, response, latency, and outcome
  • PII at the boundary — customer data lives in source systems; the orchestration backbone never persists PII in cleartext
  • Knowledge graphs complement, not replace — Neo4j stores the static catalog graph; live state stays in the system of record
Read the MCP module code standard →
04—Representative Outcomes

What a scoped engagement typically delivers.

5-8 weeks
from scoped brief to first production release
Typical engagement length for a focused MCP agent or RFQ workflow.

A scoped engagement typically puts the first agent in production in five to eight weeks.

6-10
reusable MCP modules per program
Each connector ships with tests, rate limits, and auth; reused across projects.

A first program commonly produces six to ten reusable MCP modules.

30-60%
reduction in integration glue code
Replacing per-project bespoke clients with the shared MCP module pattern.

Shared MCP modules typically remove a third to two-thirds of integration glue code.

1 flow
from request intake to quote acceptance
A single governed path from catalog discovery through supplier pricing, approvals, inventory holds, and downstream handoff.

One governed RFQ flow connects request intake, pricing, holds, approvals, and handoff.

Representative targets, not guarantees. Actual outcomes depend on scope, source-system maturity, and your team's capacity to participate.

05—Knowledge Graph

Industry knowledge, structured for instant support.

The transaction graph also becomes a support graph: supplier relationships, product hierarchies, compatibility rules, pricing tiers, and cross-reference data are available to support agents and AI assistants at the moment an inquiry arrives.

A Neo4j graph that powers both transactions and customer support — suppliers, products, compatibility, and pricing in one queryable model.

01

Industry Knowledge Model

A graph model of your industry's products, suppliers, customers, taxonomies, and relationships — built from your source systems and enriched with external standards. Supports natural-language queries like "which suppliers carry an equivalent to part X with lead time under 5 days?"

02

Support Agent Context Layer

When a customer inquiry arrives, the knowledge graph surfaces relevant context automatically — order history, product compatibility, substitute options, pricing rules, and supplier status — so support agents and AI assistants respond with accurate, relationship-aware answers instead of searching across disconnected systems. GraphRAG — retrieval that traverses relationships, not just vector similarity — surfaces substitutes and compatibility that a vector-only search misses.

03

Escalation Reduction

AI assistants query the graph to answer tier-1 questions autonomously — product specs, order status, substitute availability, and pricing tiers — deflecting routine inquiries before they reach a human. Complex cases escalate with full graph context attached, so tier-2 agents start informed.

04

Continuous Graph Enrichment

Dagster pipelines sync new products, suppliers, pricing changes, and customer interactions into the graph on a schedule. The knowledge graph stays current without manual maintenance— every support interaction queries the latest state of your industry data.

06—How We Engage

A four-step build that hands over code, not a black box.

1

Discovery

Inventory of source systems, RFQ/business workflow map, MCP tool catalog draft, agent scope, success metrics.

1 week
2

MCP Modules + Domain Model

One connector per source system plus request, quote, quote item, supplier item, pricing, availability, and policy entities as needed.

2-3 weeks
3

Agent + Orchestration

AI agent orchestration backbone, system prompt, tool descriptions, session store, RFQ workflow actions, Dagster pipeline if data sync is involved.

2 weeks
4

Hardening + Cutover

Observability, rate limits, fallback paths, feature-flag rollout, operator runbook, quote/booking/order handoff checks.

1 week
07—Stack We Build On

Model-agnostic by design.

No proprietary lock-in. The orchestration backbone and Model Context Protocol (MCP) module pattern work with any model that can call tools reliably and follow structured instructions, whether open-weight or frontier.

Frontier, open-weight, or self-hosted — the orchestration backbone routes to whichever model fits the task, cost, and compliance constraint.

RuntimeOpen-source / open-weight LLM
ProtocolMCP
GraphNeo4j
OrchestrationDagster
CloudAWS
LanguagePython
Transformdbt
WarehouseRedshift
08—FAQ

Questions we expect from technical buyers.

Are you a consultancy or a product company?
IdeaBosque is an AI platform and solutions company. We build, deploy, and operate AI agent applications for customers while providing the infrastructure, support, and ongoing enhancements required for production use. The same platform supports white-label and private deployment options when customers or partners need them.
Do you run the agent in production for us?
Yes. IdeaBosque can operate the agent as a managed platform, or deploy it privately in your infrastructure when that is the better fit. The buyer choice is operational control: let us run it, run it yourself, or start managed and move private later.
How do engagements work commercially?
Fixed-scope phases with weekly demos. A one-week Discovery produces the system inventory, workflow map, and build plan with full scope and cost before you commit to the build. Typical first builds run 5-8 weeks. We do not publish rate cards because scope varies with source-system maturity, but you will never start a phase without a fixed price for it.
Do I have to use a proprietary frontier model?
Yes. Our platform supports leading proprietary frontier models, including enterprise AI services from major providers. We also support open-source and open-weight models, giving you the flexibility to choose the model strategy that best meets your business, security, performance, and cost requirements.
Why the Model Context Protocol (MCP) instead of direct REST calls?
MCP gives every backend the same reviewed tool surface, so the agent's capabilities are easier to test, audit, and swap. Direct REST calls are fast for a prototype, but they usually become hard to govern once the second or third integration arrives.
What if my system does not have an API?
If it has a stable programmatic surface, we can usually wrap it. We have patterns for GraphQL, REST, SDKs, database gateways, and controlled file-based exchange. If the only path is manual UI automation, we call that out as a risk before build.
Is this only for retail, travel, or hospitality?
No. Travel and hospitality are strong proof cases because they combine constrained inventory, dates, occupancy, cancellation rules, FX, and supplier-specific pricing. The same RFQ-to-B2B pattern applies anywhere buyers, suppliers, quotes, approvals, capacity, and downstream transactions need to work across multiple systems.
How does the knowledge graph help customer support?
The Neo4j knowledge graph stores industry taxonomies, product relationships, supplier mappings, and customer context. When a support inquiry arrives, the AI agent queries the graph to surface relevant context — order history, compatibility, substitutes, and pricing — automatically. Tier-1 questions can be answered autonomously; complex escalations carry full graph context to tier-2, reducing resolution time and escalation rates.
How do IdeaBosque agent deployments handle AI governance and the EU AI Act?
Every tool call is logged with request, response, latency, and outcome, so the agent's behavior is auditable end-to-end — the same posture enterprises now apply to financial controls. For customers subject to the EU AI Act's August 2026 transparency obligations, the audit logs, operator runbooks, and code-ownership option support a compliance narrative; we scope governance requirements into Discovery when you flag them.
How do IdeaBosque MCP modules address the security vulnerabilities disclosed in the OX Security report?
Every MCP module we ship is tested, rate-limited, and audited — every tool call logs request, response, latency, and outcome. The OX Security disclosure identified command-injection vulnerabilities in 200,000 community MCP servers that lack these controls. Our module standard (published in the Library) requires integration tests, error-path coverage, and operator runbooks before a module ships. The kill-switch architecture means any module can be disabled without touching the orchestration backbone.
How do IdeaBosque agent deployments avoid the pilot-sprawl ROI trap?
PwC's 2026 CEO Survey found 56% of organizations report no measurable financial benefit from AI. The diagnosis across PwC, Anthropic, and OpenAI is pilot sprawl — tool access democratized, workflow redesign not. Deloitte's 2026 State of AI found only 1 in 5 companies has mature governance for autonomous AI agents. IdeaBosque engagements are the opposite of a pilot: fixed-scope phases, weekly demos, production code that posts to your system of record, and a one-week Discovery that produces the system inventory, workflow map, and build plan before you commit. The deliverable is a working agent in 5-8 weeks, not a demo that never ships.
09—From the Library

These are working notes from the same engineering practice that builds customer agents: MCP standards, connector architecture, governance, and production AI patterns.

MCP

MCP Module Code Standard

The code standard every MCP module follows: directory structure, tool registration, error handling, rate limiting, audit logging, and PII boundary rules.

July 12, 2026Security & Governance

Kill switch by design: agent governance architecture

Gartner predicts 40% of enterprises will decommission agents by 2027. The architecture that works is layered: identity-gated access, per-tool circuit breakers, tenant isolation, and rapid rollback.

July 11, 2026Strategy

Open-weight models crossed the agentic frontier

DeepSeek V4 and GLM 5.2 put open-weight models at production-viable agentic quality — the case for treating model selection as configuration, not a rebuild.

July 12, 2026
Browse all articles →
10 — Contact

Tell us what systems you're stitching together.

Send the systems involved, the workflow to automate, and the handoff you need. An engineer reads every brief and replies within two business days with a fit and risk assessment.

Send project brief