Skip to content
Discuss your project
Agentic AI Systems

AI Agent Development & MCP Architecture

Agents that operate against your business systems, designed around MCP and tool boundaries, scoped auth, eval harnesses, human escalation paths, and the rollout discipline a live agent needs.

Discuss your projectExplore the review

Agent Architecture

Runtime

User / Caller

Agent Loop

plan · act · observe

Tools / MCP

Scoped tool manifest, deterministic schemas

Auth & Scopes

OAuth 2.0, PKCE, per-tool permissions

Evals & Traces

Pre-release evals plus live trace capture

Audit Log

Every call, every approval, replayable

What this delivers

The agent ships behind a deterministic gate.

Timeline
6-12 weeks
Engagement
AI-native engineering
Output
Live agent system
Start with
A scoped engagement

Who It's For

Teams building a new AI product or extending and hardening an agent already in use.

  • Your agent prototype works in demos and fails on edge cases, permissions, or observability
  • Tool calls, auth scopes, and side effects on business systems are not yet safe to expose
  • Leadership wants a launch plan before funding a large build
  • Manual knowledge work is expensive and inconsistent, and existing automation is brittle

What We Build

Agent workflow architecture

Planner, executor, tool, memory, and human-review boundaries designed around business risk rather than framework defaults.

MCP and tool integration

Scoped tool manifests, deterministic schemas, and integration patterns that avoid uncontrolled side effects on live systems.

Auth and auditability

Per-tool permissioning, approval gates, replayable traces, and the documentation a security review will ask for.

Product experience and approvals

Approval interfaces, operational views, human escalation, role-based controls and audit trails integrated into the customer’s product.

Our Approach

Delivered by a forward-deployed team on agreed milestones, with a monthly retainer to run it after launch.

1

Map the workflow

Weeks 1-2

Identify where an agent should decide, where deterministic software should own the flow, and where a human stays in the loop. Rank candidates by operating value, failure cost, and data readiness.

2

Design the boundary

Weeks 3-4

Define tool schemas, auth scopes, approval gates, memory boundaries, and fallback paths. This is the work that decides whether an agent is safe to point at live systems.

3

Build the runtime harness

Weeks 5-9

Build the agent loop against those boundaries, then the apparatus around it: evals, trace capture, cost telemetry, error taxonomies, and the gates a release has to pass.

4

Ship the system

Weeks 10-12

Deploy behind a staged rollout with dashboards, runbooks, and rollback paths. Your team leaves able to operate and extend the system without us.

Common Questions

Can you help if we already have an agent prototype?

Yes, and it is often the fastest path. We review the prototype, preserve what works, and rebuild the runtime boundary around tools, auth, evals, and observability.

Do you require a specific agent framework?

No. Framework choice follows the workflow, your existing stack, reliability needs, and who will own the system afterward. The architecture prioritizes reliability and observability over framework features.

How do you handle agent reliability and safety?

Layered guardrails: input validation, output filtering, confirmation for high-risk actions, and automatic escalation to a human when confidence drops. Every action is logged and replayable.

How do you manage agent cost at scale?

Token budgets, caching, model routing so cheap models handle simple steps, and rate limiting. Cost dashboards give you spend per workflow within the same billing period.

Can this start before a full data platform project?

Yes. We identify which data foundations are required now, which can be deferred, and which use cases should not proceed yet.

What You Get

  • Agent architecture with MCP and tool integration
  • Auth, permissioning, and auditability model
  • Eval harness and failure-mode test plan
  • Guardrails and human escalation paths
  • Product approval flows and operational interfaces
  • Trace capture and observability pipeline
  • Cost controls and rate limiting
  • Staged rollout plan and operational runbooks
Discuss your project

AI-Native SaaS / selected work

Agent Platform Engineering for Enterprise AI

The problem
Give AI agents a way to find, create and update a content platform's structured information.
What we built
Backend AI with Anthropic, OpenAI and Google models, 50+ MCP tools, content APIs and semantic search.
What it made possible
Agents that find information, act on it and power interactive experiences inside compatible AI clients, for a unicorn startup and Fortune 500 companies.
50+
MCP tools built

Backend AI, API and infrastructure engineering, from architecture through launch.

Read case study

Agent platform engineering

  1. Backend AIAnthropic, OpenAI and Google
  2. 50+ MCP tools builtDiscover, create and update content
  3. APIs and searchStructured content and semantic retrieval
  4. Platform infrastructureSupports agents and MCP Apps
The platform's agent capabilities, layer by layer.

Start a conversation

What are you building—or deciding?

Tell us where you are. We reply within one business day, and the first conversation ends with a recommended next step.

Discuss your project