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AI & Agentic Systems

An agent is only as capable as the systems it can reach.

Most agentic pilots stall at the same wall: the model is fine, but it has no governed, permissioned, auditable route into the systems where the work actually happens. That route is an integration problem — which is the part we already do.

Ungoverned versus governed agent access to enterprise systemsTop row: an agent given shared credentials talks directly to a system of record, and stalls at security review. Bottom row: the same agent calls a governed tool layer providing identity, scope, schema validation and audit, which then reaches the system of record.WHAT MOST PILOTS DOAgentshared credentials, no scope, no logSystem of recordStalls at security review — nobody can say what it touched.WHAT ACTUALLY SHIPSAgentGoverned tool layerIdentityScopeSchemaAuditMCP servers over your existing APIsSystem of recordSame model. Same systems. The difference is a layer you can point a security review at.That layer is an integration problem before it is an AI problem.

The point of view

The model was never the bottleneck.

Almost every stalled agentic programme we see has the same shape. The demo worked. The model reasoned fine. Then it met the enterprise, and the questions started: which identity is it acting as, what exactly can it write, what happens when it is wrong, and who can reconstruct what it did last Tuesday.

Those are not AI questions. They are the questions every integration has answered for twenty years — authentication, authorisation, scope, schema validation, rate limiting, idempotency, audit. An agent calling your order system is simply an API consumer with unusually poor judgement and an unusually large action space.

Which is why we think the useful work sits in the layer beneath the model. Not prompt engineering, not another framework — a tool and context layer over the systems you already run, where each capability is defined narrowly enough that the agent cannot reach past its remit, and every call leaves a record.

Build that, and the model becomes swappable. You can change providers, upgrade to whatever ships next quarter, or run several models against the same tools, without renegotiating access to your ERP each time. Skip it, and you have a pilot that will never pass review.

Services

Where we do the work.

Three services, all of them below the model rather than around it.

Agent Tool & Context Layer

  • MCP servers over existing APIs
  • Tool schemas and scoping
  • Identity and permission mapping
  • Retrieval over governed sources

Expose enterprise systems to agents as governed tools — scoped, permissioned and logged — instead of pasting credentials into a prompt.

An agent calling your order system is an API consumer with unusually poor judgement. It needs the same treatment any consumer gets — authentication, authorisation, rate limits, schema validation, an audit trail — plus tool definitions narrow enough that the model can't reach past its remit.

Agentic Workflow Engineering

  • Human-in-the-loop checkpoints
  • Deterministic orchestration
  • Fallback and escalation paths
  • Evaluation and regression harnesses

Put agents inside real business processes, with the human checkpoints and rollback paths that make them shippable.

The judgement calls stay human; the repetitive traversal of systems does not. We design where the checkpoints sit, what the agent is allowed to commit versus propose, and how a bad run is detected and reversed — then wire evaluations so behaviour changes are caught before production.

AI-Assisted Integration Delivery

  • Schema and field mapping assistance
  • Legacy flow interpretation
  • Test and payload generation
  • Interface documentation

Use models where they genuinely compress integration work — mapping, migration and documentation — and nowhere else.

Field-level mapping across two large schemas and reading undocumented legacy flows are exactly the tasks models are good at and engineers hate. Generated output stays a proposal for review, never an unreviewed commit.

How we scope it

Start with one workflow that already annoys someone.

We would rather put one narrow agentic workflow into production than run a six-month evaluation. Pick a process where a person currently moves data between three systems and applies a rule they could describe out loud. That is the shape that works.

From there the engagement is ordinary engineering: define the tools, map them to real permissions, decide what the agent may commit versus what it may only propose, build the evaluation set, and wire the escalation path for the runs that go sideways. The human checkpoints are designed in at the start, not bolted on after an incident.

The deliverable is not a chatbot. It is a set of governed tools, a workflow that uses them, an evaluation harness that catches regressions when the model changes underneath you, and a runbook your own team can operate.

First step

Bring us the workflow that keeps getting escalated.

We'll map the systems it touches, the permissions it needs, and what a governed version would take. That map is useful even if you never build the agent.

Talk to usinfo@integronauts.com