firz.io
Approach

Solution architecture for the agentic era.

firz.io is a progressive IT consulting firm. We help enterprises make informed tradeoffs when implementing AI in production — from multi-agent orchestration and MCP integration to structured outputs, context management, and human-in-the-loop governance.

Engagement model

From discovery to steady-state operations.

01

Discover & architect

Map production constraints, integration surfaces, and where agents earn autonomy vs where humans must decide.

02

Integrate & prove

Stand up MCP tools, agent loops, and workflows against real data — measure tradeoffs, not slide-deck demos.

03

Harden & govern

Error taxonomy, policy hooks, observability, and escalation paths that hold under load and audit.

04

Operate & evolve

Runbooks, session continuity, and iterative refinement as models, tools, and business rules change.

Core competencies

What we bring to every engagement.

Our practice maps to the disciplines required for production-grade systems — not experimental chatbots.

Foundation

Agentic architecture & orchestration

Agent loops, coordinator–subagent patterns, task decomposition, session state, and hooks for deterministic enforcement when policy demands it.

Integration

Tool design & MCP integration

Clear tool boundaries, structured errors, scoped tool access per agent, and MCP servers wired to your APIs, data, and content catalogs.

Delivery

Workflow & engineering tooling

Team-wide AI development practices — shared skills, repository context, MCP in the SDLC, and integration into existing pipelines.

Quality

Prompt engineering & structured output

Schemas, few-shot patterns, extraction pipelines, and prompts that produce actionable output — not conversational dead ends.

Operations

Context & reliability

Long-document strategies, multi-turn handoffs, observability, retry discipline, and human-in-the-loop escalation design.

Principle

Production judgment

We optimize for informed tradeoffs in live systems — not proofs of concept that collapse under real ambiguity.

Principle

Architecture before automation

Orchestration, tool boundaries, and context design come first. Automation follows a clear structural model.

Principle

Governance by design

When compliance is non-negotiable, we use enforcement mechanisms — not prompt instructions alone.

Principle

Humans where it matters

Agents handle volume and speed; people retain authority on policy, exceptions, and accountability.

Delivery

Scenario-tested judgment.

We design for realistic production contexts — operational triage, engineering workflows, research pipelines, document extraction, and CI/CD integration — with clear escalation when automation should stop and a human should decide.