Solutions · AI Models & Agents
From models that think to agents that act
Autonomous agents can plan, use tools and finish work end to end. Making them safe, reliable and worth the cost is the real challenge, and where we focus.
Activo Group designs, builds and runs AI agents and the models behind them, grounded in your data and processes, with permissions, evaluation and human oversight built in.
- Agents for every function
- Copilots
- Multi-agent workflows
- Custom models
Models think. Agents act.
An AI model, such as a large language model or a forecasting model, learns patterns from data and produces answers, predictions or content. An agent wraps models in a goal-directed system that perceives its environment, plans multi-step actions, uses tools and learns from the results.
AI models vs autonomous agents
| AI models | Autonomous agents | |
|---|---|---|
| Core role | Predict, classify or generate from data | Pursue a goal: plan, decide and execute multi-step work |
| Examples | Language, vision, speech and forecasting models | Support, sales, finance and operations agents |
| Building blocks | Supervised, unsupervised and reinforcement learning | Models plus tools, memory, planning and guardrails |
| Behaviour | One input, one output | Loops until done; may use many models and tools |
| Main risk | Inaccurate or biased outputs | Wrong actions in real systems |
Anatomy of an agent
The three levels of intelligent work
Level 1
Automation
Rule-based scripts replace repetitive clicks.
Copying invoice data into the ledger.
Level 2
Augmentation
Copilots suggest the next best action; people decide.
Drafting a reply for a support agent to approve.
Level 3
Autonomy
Agents create plans and act without step-by-step prompts, within limits.
Chasing overdue invoices end to end, escalating disputes.
What makes autonomous agents hard, and how we handle it
Agents are non-deterministic: they decide their own next step, and it isn’t always clear when they’ll finish. That’s what makes them powerful, and what makes them risky in real systems. These are the challenges we design for from the start.
Reliability
Plausible but wrong
Agents can hallucinate, drift or misuse a tool. Our approach: narrow scopes, strict schemas and evaluation of every step, not just the answer.
Security
Access to real systems
An agent with broad access is a new attack surface, including manipulated inputs. Our approach: least-privilege access, scoped credentials and input checks.
Governance
Who is accountable?
Decisions need an owner and a trail. Our approach: every action logged and traceable, clear policies, and an off switch for every agent.
Cost
Loops that burn budget
Long-running agents can consume large volumes of tokens. Our approach: right-sized models, context management and cost-per-task tracking.
Context
Automating chaos
Agents fail on undocumented processes and messy data. Our approach: start with documented, data-rich workflows and fix the data first.
People
Trust and adoption
Agents can’t infer unwritten rules, and teams won’t use what they don’t trust. Our approach: explicit rules, visible plans and autonomy earned step by step.
Security and governance by design
Every agent we build has a clear permission model agreed with you before it goes live. Low-risk work runs on its own, consequential actions wait for a person, and some decisions never leave human hands.
Three levels of permission
Limits are set per agent and tightened or relaxed as trust is earned.
Controls built into every agent
- Its own identity and scoped access, never shared admin credentials
- Approval limits by value, customer and action type
- Every decision and action logged for audit
- Live monitoring with alerts on unusual behaviour
- Automatic freeze and escalation on unknown or high-stakes cases
- An off switch for every agent
Agents ready to put to work
Some agents handle a single task, like preparing a meeting brief. Others take on a role, combining several related tasks. Our catalogue covers both across the business, and each agent is configured to your tools, data and approval rules.
Customer
Serve and retain
Orchestrated
All working together
A 360 Orchestrator that coordinates agents and brings you only the decisions that need you.
Specialist agents, one coordinated system: each agent does one job well.
Agents that work with other agents
The next shift is already under way: agents that connect to any tool through shared standards, hand work to other agents, and even buy and sell on your behalf. Designing for this now avoids rebuilding later.
Connect
Standard tool access
Open protocols such as MCP let agents use your apps and data without one-off integrations.
Collaborate
Agent to agent
Agents delegate and negotiate with other agents, inside and outside your business.
Transact
Agent commerce
Agents compare, order and pay within approved limits, with full audit trails.
Interact
Beyond the chat box
Voice, image and on-the-fly interfaces generated for the task at hand.
AI models are the foundation
Agents are only as good as the models and data beneath them. Advantage comes from combining frontier models with your proprietary data, domain language and processes, not from using the same public models as everyone else.
Data
Data engineering
Clean, connected and governed data pipelines that models and agents can rely on.
Predict
Machine learning
Forecasting, classification, anomaly detection and recommendations from your data.
Ground
LLMs and RAG
Language models grounded in your documents and knowledge, with sources you can check.
Specialise
Fine-tuning
Smaller, cheaper models tuned to your domain where they match quality at lower cost.
Instruct
Prompt and context engineering
Instructions, examples and context assembled precisely for each step.
Operate
ML and LLM ops
Deployment, monitoring, evaluation and cost control for models in production.
From first idea to agents in production
Discover
Pick the workflow with the clearest value and the best data.
Design
Define the goal, steps, tools, permissions and success measures.
Build
A first agent on one workflow, with evaluation from day one.
Pilot
Run on real work with human approvals, and measure the outcome.
Scale
Raise autonomy as trust grows; add agents for adjacent work.
Start small, measure everything, and avoid overpromising.
Where we help
Agent opportunity map
Find the workflows where agents create the most value, and the order to tackle them.
Agent build and deployment
Single agents or coordinated teams, connected to your tools and data.
Security and governance
Permission models, approval limits, audit trails and monitoring.
Custom models
Machine learning, RAG and fine-tuned models built on your data.
Evaluation and AgentOps
Evaluations, AI judges, telemetry and cost control for agents in production.
Business case
Value, cost and risk modelled before you commit, then tracked after launch.
Start today
Put your first agent to work
Talk to our experts, with no obligation. We’ll map where agents can help, build a 30-day roadmap from discovery to a working agent, and model the business case before you commit.
