AI and Agents · Agentic systems

AI Agents and Agentic Systems

Intelligent agents that reason through tasks, retrieve organisational knowledge, call approved tools and coordinate multi-step workflows under clearly defined controls.

What usually brings people here

Most teams can get a language model to answer a question. The gap is everything after that: giving an agent access to real systems without handing it the keys, knowing what it actually did, and being able to tell whether it is getting better or worse over time.

How we approach it

We treat an agent as a software system, not a personality. It has a defined objective, a bounded set of tools, scoped credentials, a memory model, and an evaluation suite that runs before anything ships. Sensitive actions route to a person for approval, and every step is logged in a form you can audit.

Agent request path

  1. 1

    Objective

    A question, ticket, alert or schedule

  2. 2

    Plan

    Decomposed into steps

  3. 3

    Retrieve

    Grounded in permitted content

  4. 4

    Call tools

    Scoped credentials, rate limited

  5. 5

    Human approval

    Irreversible actions pause here

  6. 6

    Log and evaluate

    Full trace, quality, spend

The approval gate is what separates a controlled agent from an unattended one.

Orchestration and task planning

An agent that can decompose an objective into steps, choose the right tool for each, and recover when a step fails — rather than a single prompt hoping for the best.

  • Single-agent and multi-agent workflows
  • Task decomposition and re-planning on failure
  • Deterministic fallbacks for steps that must not be improvised
  • Concurrency and timeout controls

Tools, integrations and permissions

Agents become useful when they can act on real systems. They become safe when each of those actions is scoped, logged and revocable.

  • Tool-using agents over internal APIs, databases and SaaS platforms
  • MCP server integrations for reusable tool surfaces
  • Least-privilege credentials issued per agent, not per environment
  • Human-in-the-loop approval gates on sensitive or irreversible actions

Knowledge, retrieval and memory

Grounding an agent in your own documents and data is what separates a plausible answer from a correct one.

  • Retrieval over document repositories, wikis and operational data
  • Short-term context management and long-term memory design
  • Source citation so answers can be checked
  • Access control that follows the user, not just the agent

Evaluation, guardrails and observability

You cannot operate what you cannot measure. Evaluation runs in CI, and production behaviour is traced the same way any other service would be.

  • Task-level evaluation suites with regression gates
  • Input and output guardrails, including refusal behaviour
  • Tracing across model calls, tool calls and retrievals
  • Token and spend monitoring per agent and per workflow

What you receive

  • Agent architecture and permission model, documented
  • Working agent deployed to your AWS account
  • Tool integrations with scoped credentials and audit logging
  • Evaluation suite wired into the delivery pipeline
  • Runbook, escalation paths and handover session

What should change

  • Multi-step processes completed without manual coordination
  • Staff time moved off routine retrieval and data entry
  • A clear record of what the agent did, and why
  • A safe path to widening the agent's remit once it has earned it

AWS services we commonly use

  • Amazon Bedrock
  • AWS Lambda
  • Amazon OpenSearch Service
  • Amazon DynamoDB
  • Amazon API Gateway
  • AWS Step Functions
  • AWS Secrets Manager
  • Amazon CloudWatch

Talk this through with an engineer

Thirty minutes is usually enough to establish whether this is the right service for your situation.

Book a Strategy Session