Agentic AI & Automation

AI Agents

We build agents that read from and write to the systems your business already runs on, ERP, CRM, ticketing, document stores, internal APIs, completing multi-step tasks end to end. Every action runs inside your permission model, and every consequential step can require a human approval.

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The business problem

Assistants that only produce text move the work, they do not remove it: someone still has to copy the answer into the system where it counts. The processes that actually cost money are multi-step and cross-system, reconciling an invoice, triaging a ticket, qualifying a lead, closing a period. Automating them has historically meant brittle rule engines that break whenever a form, a vendor or an exception changes. Agents can absorb that variability, but only if they are given real system access, and real system access is exactly what makes them a security and governance problem rather than a demo.

What we do

We start from a process, not a model. We map how the work is done today, where it branches, and what a correct outcome looks like, then build an agent with a bounded toolset scoped to exactly those systems. Authorization is enforced on the agent's behalf by your own identity provider, consequential actions are gated behind human approval, and every run is logged and evaluated so accuracy is measurable rather than assumed.

AI Agents

Capabilities

Custom AI Agents

Built for one process, with a toolset scoped to the systems that process touches.
Multi-Agent Systems

Specialist agents coordinated by an orchestrator, with explicit handoffs and stop conditions.
Tool-Using Agents

Typed tool interfaces over your APIs, with validation on every call and argument.
AI Copilots

Assistance embedded in the tool a team already uses, rather than another window to check.
Workflow Agents

Long-running processes with durable state, retries and resumption after failure.
Human-in-the-Loop Approval

Configurable gates so consequential actions wait for a named person.

Common use cases

Invoice reconciliation across an ERP and a supplier portal, with exceptions escalated to finance.
Support ticket triage that reads history, classifies, drafts a reply and routes to the right queue.
Lead research and CRM enrichment ahead of a sales call, with sources cited for every claim.
Period-close checks that gather evidence from several systems and flag only what needs judgement.

How we deliver

Process discovery

Map the current path, its branches, its exceptions and its definition of correct.
Bounded prototype

One process, read-only first, measured against a labelled set before it writes anything.
Guardrails & approval

Permissions, rate limits, approval gates and audit logging before production access.
Production & operate

Staged rollout with continuous evaluation, cost monitoring and a rollback path.

Technology

OpenAI
Anthropic
Google Vertex AI
Azure OpenAI
AWS Bedrock
Model Context Protocol
LangGraph
Temporal
PostgreSQL
OpenTelemetry

Security & governance

An agent with system access is a privileged identity and is treated as one. It authenticates through your identity provider, holds least-privilege scopes, and cannot exceed the permissions of the user on whose behalf it acts. Tool calls are validated and rate-limited, untrusted content is treated as data rather than instruction to limit prompt injection, and consequential actions require human approval. Every run is logged with inputs, tool calls and outputs, which is what makes the system auditable under ISO/IEC 42001 and the EU AI Act.

Engagement models

AI Project

We take responsibility for designing and delivering a defined AI solution.

Forward-Deployed AI Team

A cross-functional AI team works inside your organization to continuously find and deliver opportunities.

Dedicated AI Team

Long-term dedicated engineering capacity built around your stack and delivery model.

Managed AI

We operate, monitor and continuously improve production AI systems.

Why TeamExtension.ai

Agents are an integration and operations problem wearing an AI hat. The hard parts are permissions, state, failure handling, evaluation and cost, all of which are ordinary engineering disciplines we have practised for years across enterprise systems. We can prototype in weeks, and then keep operating what we built, because the same organization supplies the long-term engineering team.

Selected clients

Frequently asked questions

How is an agent different from a chatbot?

A chatbot returns text. An agent takes actions in your systems, reading records, calling APIs, writing updates, and can chain several steps to finish a task. That capability is why an agent needs an authorization model and an audit trail, and a chatbot does not.

How do you stop an agent doing something harmful?

Through least-privilege scopes, a bounded toolset, validation on every tool call, rate limits, and human approval gates on consequential actions. An agent cannot exceed the permissions of the identity it acts under, so the blast radius is bounded by design rather than by the model behaving well.

How do you measure whether an agent is accurate enough?

We build a labelled evaluation set from your real cases before writing the agent, then score every change against it. That set becomes a regression suite, so a model upgrade or prompt change cannot quietly degrade behaviour.

Can agents run on our own infrastructure?

Yes. We deploy into your cloud tenancy or on-premise environment, and can use self-hosted open-source models where data residency or isolation requires it. Provider portability is designed in, so hosting is a deployment decision rather than a rewrite.

What does a first engagement usually look like?

One process, scoped to a bounded prototype over four to eight weeks, read-only at first and measured against your own cases. If the numbers hold, we extend it to write access with approval gates and take it to production.

Which systems can agents connect to?

Anything with an API or a database, including ERP, CRM, ITSM, document repositories and internal services. Where no API exists we build an integration layer rather than driving a user interface, because interface automation is brittle and hard to audit.

Related capabilities

Agentic AI & Automation

AI Automation

Automate the cross-system processes that rule engines could never handle reliably.

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AI Applications & Knowledge

Enterprise Knowledge AI

Turn fragmented internal knowledge into secure assistance that respects existing permissions.

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AI Applications & Knowledge

AI Integration

Connect AI to the ERP, CRM and data systems where the work and the records actually live.

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AI Security

AI Security Testing

Test AI applications for the failure modes conventional application testing does not cover.

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Discuss Your AI Initiative

Software that completes multi-step work inside your systems, under your access controls and your approval rules.

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