Agentic AI
A Demo Is a Weekend. Production Is the Year
The agent that impressed the steering committee ran against a document store, with one user, and no consequences. Production means access to the systems that run the business, evaluation that catches regression before a customer does, an escalation path for when the agent is wrong, and a cost model that survives contact with finance. That is the engineering, and it is what we do.
Frontier Capability Is Only Worth What It Can Reach
We can reach the system of record
An agent is worth what it can act on. Most pilots run against documents because that is the part nobody has to approve. Crossing into the ERP, the database, and the ticketing system is a governance and access problem before it is an engineering one, and it is the problem we are known for solving.
Model agnostic, deliberately
Models change on a quarterly cadence. The evaluation harness, the access model, the orchestration layer, and the escalation design outlive any of them. We build so that a model swap is a configuration decision rather than a rebuild.
Governed from the first sprint
Evaluation, audit trail, human escalation, and cost attribution are designed in at the start. Regulated clients never get to add these later, and the organizations that treat them as a phase two invariably build the thing twice.
Advise
Advisory
Build
Implementation
Migrate
Migration
Run
Managed Services
Extend
Custom Development
Optimize
FinOps
Six Stages, and Most Programs Skip Four of Them
Discover and prioritize
Use cases identified, scored on value and feasibility, and sequenced against what the data and access model can actually support today. Most candidate use cases fail here, which is cheaper than failing later.
Design
Agent boundaries, tool definitions, orchestration pattern, and the decision on what the agent may do without a human in the path. Architecture, not prompt writing.
Build
Implementation against real systems with real permissions, including retrieval sources, tool integrations, and the connective work back into the estate.
Evaluate
Evaluation harnesses and regression suites so that a model version, a prompt change, or a data drift does not silently degrade a production workflow. This is the stage that separates a product from a demo.
Govern
Guardrails, refusal handling, escalation paths, audit trail, and model risk documentation, implemented against the AI Governance Framework rather than invented per project.
Operate
Production monitoring, continuous evaluation, incident response, and cost attribution through AI FinOps. Agents degrade quietly, so somebody has to be watching.
What we deliver
Foundations
-
Cloud deployment
-
Identity & least privilege
-
Secure connectivity
-
Logging & auditability
-
Cost controls
-
Data residency compliance
Retrieval
-
Source governance
-
Vector search
-
Knowledge preparation
-
Embedding strategy
-
Source-to-output lineage
-
Data-layer access controls
Agent build
-
Use case prioritization
-
Tool orchestration
-
Multi-agent workflows
-
Human-in-the-loop design
-
Enterprise integration
-
Production rollout
Assurance
-
Evaluation framework
-
Regression testing
-
Guardrails & refusals
-
Monitoring & drift detection
-
Incident response
-
AI FinOps tracking
Where programs fail
Six Reasons an Agent Never Leaves Pilot
We have seen the same failures often enough to name them. None of them are model failures.
The pattern is consistent. Organizations invest heavily in prototypes, demonstrations, and proof-of-concepts, yet underinvest in the foundations required for production success. Data quality, governance, ownership, access controls, lineage, and operational monitoring are often treated as secondary concerns. As a result, promising pilots struggle to scale beyond isolated use cases. The challenge is rarely the model itself. It is the absence of the enterprise capabilities needed to support it. Moving from a successful demo to a reliable business system requires disciplined execution, and that unglamorous work represents most of the effort.
The recurring six
- The agent cannot reach the data that would make it useful
- Security will not approve the access it needs, and nobody designed an alternative
- No evaluation harness, so nobody can prove it still works after a change
- No escalation path, so the first wrong answer becomes a policy incident
- Cost was never modeled, and the pilot fails budget review rather than technical review
- No owner in the business, so the agent has no one to answer to
Every one of these is designed for, or it is discovered in production
Patterns We Have Already Built
Document Intelligence
Extraction, classification, and validation across unstructured enterprise documents, with confidence thresholds and human review routing.
Invoice Automation
Straight-through invoice processing with exception routing and a complete audit trail for finance and audit.
Service Desk Agent
First-line support resolution and triage with defined escalation to human agents, across voice and text channels.
Configure, Price, Quote
Quote generation and configuration logic driven by an agent working over product, pricing, and eligibility data.
Find out which agent use case you can actually put into production
What you walk away with
-
Current state data and infrastructure architecture review
-
AI readiness scoring across all four pillars
-
Five to eight use cases identified, scored, and sequenced
-
Access and governance gap analysis per use case
-
Phased roadmap with investment and resource estimates
-
Recommended first project scope and kickoff plan
[email protected] · infolob.com
