Agentic AI Solutions
That Turn Business Goals Into Autonomous Action.
Move beyond scripted automation with intelligent AI agents that reason through complex processes, make context-aware decisions, and take action across your enterprise systems, with governance, guardrails, and human oversight built in.
What is agentic process automation?
An AI agent is given a business goal rather than a script. It reasons over context, picks its own next action, works across whatever systems it needs, and escalates when it isn't confident enough to proceed. That's the whole difference. Where a bot breaks on the unusual case, an agent thinks about it.
We don't run agents as pilots. Before anything goes live, we scope it, write down exactly which actions it's allowed to take, register every system it depends on with AURA, and set the logging to a standard that'll survive a control review two years from now. That's what makes AutomationCOE an agentic process automation platform rather than a demo you're left to productionise on your own.
What you get isn't a chatbot. It's a digital worker with a job description, a defined blast radius, and a business case attached to it. There are 235+ running in production today.
CHALLENGES ADDRESSED
- Agent pilots that look brilliant in a demo and fall over on real operational variance
- No governance model for autonomous decisions, so nobody can answer "why did it do that?"
- AI agents with no dependency awareness, failing quietly when an upstream system changes underneath them
- Scope and cost that grow without anyone being able to attribute ROI to a specific agent
How we deploy enterprise AI agents without losing sleep
Agentic automation rarely fails because the model isn't clever enough. It fails because nobody decided what the agent was and wasn't allowed to do. So we decide that first.
Agent candidacy assessment
Plenty of processes shouldn't be agentic at all. We score candidates on how much variance and judgement they actually contain, which usually separates real agent work from something a cheaper deterministic bot would handle fine.
ARIA goal and context extraction
ARIA reads your process recordings and documentation, then pulls out the agent's objective, its decision boundaries, the tools it needs, and what success looks like. You get a specification. Not a prompt.
Action policy definition
Every action, system, and data boundary gets written down explicitly. Autonomy is granted action by action rather than agent by agent, and anything irreversible needs a human in the loop.
Agent build and tool integration
We build against your real systems, your existing bot estate, and your APIs. Every tool call is typed, validated, and rate-bounded.
Evaluation and confidence calibration
The agent gets tested against a golden set of real historical cases, including the ugly ones and a few adversarial inputs. Confidence thresholds are then tuned to what being wrong actually costs in that process, which varies enormously.
AURA runtime governance
AURA watches the reasoning traces, the tool calls, and every dependency the agent relies on. Drift gets flagged, anomalies get escalated, and the decision trail stays intact.
Key Capabilities
Autonomous AI agents, bounded on purpose
Agents built around a business objective and a defined set of actions, rather than an open-ended prompt and optimism.
- Objective and success criteria definition
- Explicit tool and system inventory
- Decision boundaries and escalation design
- Human-in-the-loop gate placement
Multi-agent orchestration
Bigger processes broken across specialist AI agents, with an orchestrator that owns the outcome.
- Task decomposition and agent specialisation
- Orchestrator and handoff design
- Shared state and memory architecture
- Conflict and deadlock resolution
Agent governance and auditability
Every reasoning step, tool call, and decision captured well enough to satisfy a regulated review.
- Full decision and reasoning trace logging
- Action-level approval and authority controls
- PII detection and anonymisation
- Audit export in a format regulators accept
Working with your existing bots
Agents that direct the bot estate you've already paid for, instead of replacing it.
- Agent-to-bot invocation across UiPath, AA, Blue Prism, Power Automate
- Legacy system access through existing automations
- Hybrid deterministic and agentic process design
- Sequencing for migrating a bot to an agent
Business Value
Agentic AI solutions earn their keep by absorbing the exception volume your bots currently hand back to people.
Autonomous AI agents in production
Running live across enterprise environments today. Not in pilot.
Faster from scope to specification
ARIA writes the agent specification, which removes the discovery and requirements cycle that stalls most agent programmes before they start.
Decisions traceable
Every autonomous action carries a reasoning trace, an authority reference, and a timestamp.
Unbounded actions
No agent holds authority it wasn't explicitly given. We define the blast radius before deployment rather than after the incident.
Where this applies
Exception-heavy case resolution
KYC discrepancies, document mismatches, payment investigations. The cases your bots route to a person because the format didn't match. Full decision trails for the regulator.
Claims denial triage
Agents read the denial reason, go and find the supporting clinical documentation, and assemble the appeal. Only genuinely clinical judgement gets escalated.
Autonomous incident response
Alerts triaged, correlated across systems, known remediations executed. When the agent can't resolve something, it hands over with a complete diagnostic package rather than a ticket number.
Regulated document assembly
Submission documentation assembled and cross-checked inside validated environments, with every source and every edit attributable.
Customer results
Tier-1 retail bank, 14 markets
Loan origination on an 11-day cycle across 14 markets, with document format variance forcing large volumes into manual review.
Cycle time down to 9 hours, a 96% reduction. Straight-through processing at 84%. No auditor flags.
Global shared services, 90+ entities
Alert volume triaged by hand across 90+ entities, with no consistent diagnostic package when anything got handed over.
AURA prevented 412 production incidents in the first 90 days. MTTR down 71%. Uptime at 99%.
