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For product & engineering · AI-driven product development

Agentic AI Architecture

Design multi-agent systems that hold up in production, not just in the demo.

Architecture for agents that plan, call tools and hand off work, with the controls to run them at enterprise scale.

Schedule a discovery call See case studies
The Challenge

Most agent projects start as one prompt and a few tools. As tasks grow, agents loop, call the wrong tool or lose context, and nobody can say why a run failed.

The architecture

Agents that fail safely and still finish the job.

Frontend · your app
You
OR
Orchestrator · routed to
Backend · agent runtime orchestrator · workers · state
Scenario
run.log
Harness engineering and hardening Defenses light up as they catch failures in the run above.
Hardening
Agent harness
PlannerContext managerMemory
Modelopen or closed LLM
Tool router · MCPSub-agent spawnerOutput parser

Platforms we work with

We build on the agent stack you've chosen.

What stays the same

  • Your cloud, identity and security controls
  • Your data sources and system permissions
  • The tools your teams already use

What changes

  • One architecture pattern across teams
  • Every agent run traced and replayable
  • Clear limits on what each agent can do

LangGraph and LangChain

For stateful, multi-step agents with explicit control flow, checkpoints and human-approval steps.

Amazon Bedrock, Azure AI Foundry and Google Vertex AI

Managed agent and model services inside the cloud you already run, under your existing security controls.

Model Context Protocol (MCP)

A standard way to connect agents to tools and data, so new systems plug in without custom glue code.

Your own services and APIs

Agents call the systems you already have, with the same permissions and audit trail as your people.

What the agent does

From signal to action, with
your team in control.

Map the work

Route each workflow, plan decisions, and decide which need an agent and which need plain code.

Choose the pattern

Single agent, planner-checker, or a supervised team, picked for the task, with clear limits on each agent.

Connect tools and data

Tool calls, schemas and memory designed with permissions, timeouts and fallbacks.

Make it observable

Every run stored step-by-step, so history can be replayed and fixed.

When to call us

Signs this is the right time.

Find a problem you recognize on the left. Read across to see which solutions address it.

If you're seeing... We build the AI agent We prove they pay back
An agent prototype that won't scale past the demo✓✓
Several teams building agents in different ways✓
Agents that loop, stall or call the wrong tool✓
A plan to put agents in front of customers✓✓

Take your AI from pilot to production.

Build, validate, and deploy AI that delivers real business impact.

Schedule a discovery call