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Agentic AI Systems

AI that acts, not just responds.

Multi-agent pipelines. Tool use. Autonomous workflows.

10×
Workflow automation gain
< 5%
Agent failure rate
3+
Agentic systems shipped
Full
Audit trail
Fibre patch leads fanned out of a switch in a dark cabinet

Overview

Agentic AI goes beyond chatbots. We build multi-agent systems that plan, use tools, call APIs, write code, and complete multi-step tasks autonomously. For enterprise workflows, this means AI that can process a procurement request end-to-end, or conduct research and draft a report without human intervention at every step.

The Problem

Single-turn LLM responses are useful but limited. Real enterprise workflows require multi-step reasoning, tool use, and state management across actions. Building reliable agentic systems is hard — agents hallucinate tool calls, get stuck in loops, and fail ungracefully on edge cases.

Our approach

We design agentic architectures with explicit planning steps, tool validation, and graceful failure handling. We use LangChain and custom orchestration for multi-agent coordination, with structured output validation at every tool call boundary. Human-in-the-loop checkpoints for high-stakes decisions. Comprehensive logging for auditability.

Deliverables

  • Agentic workflow architecture
  • Multi-agent orchestration
  • Tool and API integration
  • Human-in-the-loop design
  • Audit logging
  • Failure handling and retries

Tech stack

LangChainLlama 3 70BGPT-4ClaudeFastAPIRedisBullMQPostgreSQLDocker

How it runs

An agent that can be audited.

An agent is a loop with tools, and the part that decides whether it is shippable is not the reasoning — it is what happens when a step fails.

01 · plan

Plan

The task is decomposed into steps against an explicit tool inventory, with the permitted actions fixed before the run starts.

02 · act

Call a tool

Each call is typed and recorded with its arguments. Nothing reaches a production system that is not on the inventory.

03 · verify

Check the result

Output is validated against the expectation for that step. A failed check retries or stops — it does not carry a bad result forward.

04 · hand-off

Escalate to a human

Anything outside the agent’s authority stops and is handed to an operator with the full trace attached.

A schematic of the agent loop we build to. Tool inventories, thresholds and escalation rules are defined per engagement.

In practice

Authority is the design decision, not the model

An agent is defined by what it may do without asking. We draw that line at reversibility: a step that can be undone runs unattended, a step that cannot stops at a person. ICCS is the strict version of it. Detection and grading are automatic, a policy layer of sector map and cooldown decides whether a response is warranted at all, and dispatch selects the best available drone — while launch and abort remain under operator command. Autonomy runs up to the irreversible action and no further.

The plan lives in the database, not the context window

A language model is a poor record of what it has already done. Run state is held in Postgres — each step a durable row with an idempotency key, its arguments and its result — so a retry repeats a call rather than a mistake, and a restart resumes instead of beginning again. A state machine governs transitions and step budgets: the model proposes, the machine decides what is legal. In YouOps that structure also carries cost-aware routing across four model providers, so a cheap model triages and an expensive one only sees the hard step.

Where agents fail, and how it surfaces

Agents rarely fail loudly. They loop. They call the right tool with plausible, wrong arguments. They report success on a partial run, and they break quietly when an upstream contract changes shape. So every tool boundary validates structured output against a schema and a scope, not just a type. Step budgets and cooldowns end loops. A failed step raises rather than retrying in silence. The run log records the plan, the arguments, the response and the operator action — tamper-resistant, exportable, written for the assessor who will read it.

What the operator and the auditor each need

An agentic system is usually operated by someone who did not choose it. That shapes the interface: a graded queue, an explicit acknowledge, and an abort that works at every stage — the ICCS pattern. The auditor needs something different and simpler, which is one system with one log, because in an accreditation review five vendors is five arguments. At handover you take the orchestration code, the tool contracts, the escalation rules and the runbook, tested against our 80% unit coverage floor.

Where this has run

ICCS

A five-stage loop in production — detect, alert, policy, dispatch, operate — fusing eight AI detection models and commanding up to four autonomous drones over MAVLink and ArduPilot.

80 configured cameras on a single GPU node, alerts graded and broadcast in 50 ms batches, launch and abort under operator command. Release v1.25.2, 27 June 2026.

YouOps

Multi-agent content operations: topic to research to script to audio to task assignment to metadata, with cost-aware routing across four model providers.

Spec v1.2, single-instance deployment, in production use — our own content operation runs on it, which is how the failure modes were found.

Neurobloom

Guided consultation and personalised recommendation in place of a category tree, on a conversational stack with a fine-tuned model.

58% higher average order value, 71% consultation completion, and cross-border expansion into the UAE inside four months.

Questions

What buyers ask about this specifically.

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