Agentic Systems & AI Automation Consulting

Developing an agentic AI system is an engineering discipline, not a prompt-writing exercise. Our approach starts with architecture: we map the agents required, the contracts between them, the tools and models each one is assigned, and the orchestration logic that coordinates the system. We then design the infrastructure layer (memory, observability, audit trail) before any agent is built. Agents are written as production code with retry logic, structured I/O, error handling and state management. Every agent is benchmarked against a human baseline using a golden dataset before it gets autonomy. The output is a working multi-agent system with documentation, dashboards and a governance framework, not a stack of prompts behind a no-code tool.

AI automation typically means deterministic workflows with AI embedded at specific decision points: an AI step inside a Zapier or n8n flow, an AI-enriched HubSpot workflow, a Breeze AI assistant that summarises a deal record. The flow itself is predefined, the AI just makes one or two decisions inside it. Agentic AI is the opposite: the AI agent decides what to do, in what order, using what tools, within boundaries set by the system architecture. AI automation is appropriate when the workflow is well-understood and can be hard-coded. Agentic AI is appropriate when the work involves judgement, multi-step reasoning or coordination across systems. Most production-grade financial services AI estates need both, used deliberately, not one applied where the other fits better.

Human-in-the-loop is a design principle, not a checkbox. In practice it means three things. First, a validation framework: every workflow the system is meant to handle has a documented human baseline before any agent is allowed to run it autonomously. Second, gating: workflows are classified into autonomy levels, full autonomy, approval-required, recommended-only, manual-only, based on risk and confidence. Third, an override mechanism: every agent has a documented stop, audit and override path that a human can invoke at any point. Without all three, "human-in-the-loop" is marketing language. With all three, it is the difference between an AI system your compliance officer signs off on and an AI system your compliance officer shuts down.

More than most teams plan for. A production agentic system needs an orchestration layer that routes work between agents and handles state. An observability layer that captures logs, traces and agent decisions in a queryable form. A memory layer that holds context across runs. Tool and API access via MCP (Model Context Protocol) or equivalent. A control plane for permissioning, override and audit. A benchmarking pipeline that compares agent outputs against the golden dataset on every release. A KPI framework that measures accuracy, latency, intervention rate and drift over time. We design and build all of this as part of the engagement, either deployed onto your infrastructure or hosted as middleware and serverless functions where persistent infrastructure is part of the scope.

Compliance is designed into the architecture, not retrofitted after the agents are running. Every agent has a documented purpose, data sources, model assignment, tool permissions and override path. Every agent action is logged with full lineage so audit trails can be reconstructed. The system is scoped against FCA principles, Consumer Duty obligations, SMCR personal accountability and your internal risk appetite before build begins. Workflows touching protected categories (advice, decisioning, customer communications) are subject to stricter gating and validation than internal-facing workflows. The governance framework is documented as part of the engagement, so internal audit, external auditors and the FCA all have a defensible record of how the system was designed and what controls are in place.

Yes, with two distinct approaches. Inside the platform: we activate and configure native AI features (HubSpot Breeze AI, Salesforce Einstein), build custom Breeze assistants and custom workflows that use the platform's native AI. This is the right approach when the work belongs inside the CRM. Above the platform: when the work spans multiple systems or requires capabilities beyond what the platform's AI exposes, we build agents and middleware that sit above the platform and integrate through APIs and MCP. Most production financial services AI estates use both. For deeper HubSpot AI implementation specifically, see our HubSpot Services page.

A focused single-agent or platform-AI activation typically runs between 4 and 12 weeks. A multi-agent architecture build with infrastructure, observability and human-in-the-loop validation typically runs between 12 and 24 weeks, depending on the number of agents, the systems they integrate with and the depth of governance required. Engagements are scoped after the AI Strategy & Readiness phase, where we score readiness across data, process, platform, governance and team. The readiness assessment determines whether the build is feasible, how it should be phased and where the risk concentrations are. Most firms get more value from a smaller, fully governed multi-agent system than from a larger ungoverned one, so we tend to recommend tighter initial scopes and structured expansion.