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Artificial intelligence has moved from research labs to business engine rooms in under a decade. Chatbots triage customer queries, large language models draft code, and machine-learning pipelines now predict demand more accurately than whole forecasting teams once could. IT operations are next in line: the sheer volume, velocity, and variability of cloud-native estates demand decision-making speeds far beyond human reflexes.
Enter agentic AI - systems that not only automate a script but also pursue goals, reason about context, and act autonomously within policy guardrails. Think of dozens of specialised micro-agents watching every metric, log, and trace; collaborating in real time; and remediating - or even preventing - incidents before an on-call engineer’s pager so much as vibrates. This shift from reactive playbooks to proactive, self-optimising estates is more than another step in automation: it is a change in operating model, risk posture, and how organisations allocate human creativity. It’s a core pillar of cloud modernisation, setting the stage for resilient, adaptive IT.
Agentic AI collapses the latency between detection and action, drives down toil by orders of magnitude, and simultaneously unlocks continuous optimisation across cost, performance, and compliance. In practice, that means cloud costs are optimised automatically during quiet hours, SLOs that are adjusted on the fly to meet business priorities, and audit evidence generated in real-time as every agent decision is logged and signed.
From our vantage point at Deimos - helping the most innovative companies across Africa to get maximum value from their cloud real estate - we are pioneering and working with early adopters, turning agentic pilots into strategic differentiators. Organisations that invest now in unified telemetry, policy engines, and explainable AI pipelines will be poised to let autonomous agents shoulder the midnight firefights while their engineers focus on innovation and growth.
The hype around “self-healing” ops is loud, but the data shows that most teams are still at step zero of true autonomy. A snapshot:
The result is a reactive operating model: humans sift through alerts, open a ticket, copy-paste a playbook and hope nothing breaks in the meantime. The mean time to recovery (MTTR) is still over an hour for 82 % of teams surveyed in 2024.
At Deimos, we see the same pattern across African telcos, fintechs and global SaaS clients: available data, too many tools, thin guardrails. That is the launchpad for agentic systems - but also a reminder that maturity work (data hygiene, unified policy, skills) must come first before the shiny AI agents can safely take the controls.
Before anything can be automated, the estate has to be seen. Stage 1 is about corralling metrics, logs, traces and events into a single telemetry lake, tagging them with consistent metadata and surfacing the four golden signals (latency, traffic, errors, saturation). Yet only 25 % of organisations report true full-stack observability, and those that have it already enjoy 79 % less downtime and 48 % lower outage costs.
Tool sprawl remains rampant. Teams juggle half a dozen dashboards and miss correlations hiding in plain sight. Deimos typically starts by rationalising collectors and wiring everything into OpenTelemetry-native pipelines so later AI agents have rich, clean data to reason over.
With data in one place, machine-learning models can spot anomalies, forecast capacity, and recommend the best remediation, but humans still press enter. Most commercial AIOps suites live here today: they prioritise alerts, link incidents to probable root causes and suggest runbook steps. ServiceNow’s research confirms enterprises are “looking to use automated remediation on tasks that are repeatable and well defined,” yet adoption is still confined to notifications and dashboards
Here, specialised agents execute the fix themselves - but only via a safe-action pipeline that enforces policy gates, logs every step, and allows an engineer to veto or rollback. Closed-loop remediation is already eliminating whole classes of “known” incidents at hyperscalers, but regulators insist on human oversight for anything deemed high-risk. Article 14 of the EU AI Act makes that explicit, requiring organisations to keep a “human on the loop” and maintain full audit trails.
Deimos bakes those guardrails into Terraform modules (OPA/Kyverno policies, change-window checks, cryptographically signed logs) so autonomy never outruns governance.
The end state is a mesh of negotiating agents - capacity optimisers, fin-ops brokers, security sentinels - continually trading off cost, risk, and service-level objectives without human micromanagement. Forbes notes that multi-agent systems are already being piloted to balance sustainability targets against compute budgets in real time.
Research into agent negotiation frameworks shows similar swarms buying and releasing cloud resources minute-by-minute based on SLO adherence and carbon intensity. Every decision is signed, version-controlled and fed back into the learning loop, yielding estates that self-optimise across performance, spend and compliance.
All telemetry - metrics, logs, traces, events - lands in a single, schema-governed store. Deimos favours an OpenTelemetry based pipeline feeding long term analytics tools like ClickHouse / BigQuery and tools like Loki or Grafana Mimir for hot queries, eliminating tool sprawl and giving later ML stages clean, labelled data.
Here, statistical anomaly detection meets knowledge-graph and causal-inference models. They pinpoint why something failed (not just that it did) and predict when it will happen next. Kubeflow pipelines for reproducibility can wrap micro-services for LLM-style reasoning as well as classical time-series models deploying tools like LangGraph and Ray Server
A swarm of purpose-built agents - autoscalers, patch-bots, cost-optimisers - coordinate via a lightweight Agent-to-Agent (A2A) protocol. Each agent has a bounded scope and a verifiable policy contract. A properly designed system allows for swapping agents without rewriting the core orchestration fabric.
Every proposed action is evaluated against organisation policy: RBAC, change windows, budget ceilings, compliance zones. Cloud security architecture must underpin this layer to ensure decisions remain within regulatory and organisational risk appetite. OPA/Kyverno policies are embedded directly into the mesh. A self-service dashboard can also be created where risk teams adjust rules without redeploying code.
Approved plans are materialised via GitOps: the agent commits to a repo, CI validates, ArgoCD/Flux applies, and drift detection confirms the change. This model provides cryptographic audit trails that satisfy EU-AI-Act “human-oversight” rules and slash mean-time-to-recover by automating rollbacks if post-deployment SLOs dip.
Ultimately, whether it’s Kubernetes clusters, serverless functions, or classic VMs, the underlying estate becomes the substrate the agents manipulate - always through IaC so the whole stack remains declarative and reversible.
Make a fast, ruthless audit: Which metrics, logs, traces, and events actually power incident response, which are noise, and where are the blind spots? Consolidate collectors under a single OpenTelemetry pipeline, normalise labels (service, version, region), and backfill critical gaps—e.g., user-journey latency or cost per request.
Autonomous agents without guardrails are just fast chaos. Deploy an organisation-wide policy store (OPA, Kyverno or Cedar), codify the basics - RBAC, change-window curfews, budget ceilings, residency zones - then expose a self-service UI so security and finance teams can tweak rules without filing tickets.
Pick a task that is repetitive, well-defined and high-impact when it fails - e.g. patching vulnerable AMIs, right-sizing pods after traffic spikes, or auto-rotating leaked AWS keys. Wrap it in a safe-action pipeline: the agent proposes a plan, CI validates it, a human can veto, and rollback is automatic if SLOs dip. Success metrics (MTTR, toil hours, cost delta) should be tracked from day one; these hard numbers are what will win executive buy-in for the next stage.
Treat the agent’s reasoning chain and the resulting infrastructure diff as artefacts on par with code. Store them in Git, sign them, and pipe them into an immutable log (e.g. Parquet in object storage). This satisfies EU AI-Act auditability, feeds continuous-learning loops, and gives engineers forensic visibility if things go sideways.
Engineers become policy designers and failure-mode reviewers rather than button-pushers. Run tabletop simulations where staff inspect an agent’s proposal, challenge its assumptions, and edit policies to close loopholes.
After each pilot cycle, log new failure modes or governance gaps, update the policy set, and promote successful patterns into reusable “playbooks” that next projects can inherit. One way to do this is creating and maintaining a shared, Git-backed risk register so lessons from one domain (e.g. fin-ops) flow into another (e.g. security hardening) without repeating mistakes.
Publish a quarterly “autonomy scorecard”: downtime saved, cost optimised, manual tickets retired, compliance findings closed. Concrete deltas build momentum far faster than glossy decks.
Deimos takeaway: the technology stack is the easy part; disciplined telemetry, enforce-first policy, and relentless feedback loops are what let agentic AI scale from a shiny demo to a cornerstone of resilient, cost-smart operations.
Agentic AI won’t replace your operations teams - it will transform their role into strategic architects of resilience and efficiency. The organisations that begin now, anchoring their journey in robust guardrails, unified telemetry, and explainable decision-making, will be tomorrow’s leaders in cost-optimised, self-healing infrastructure. At Deimos, we’re not just theorising - we’re actively building these autonomous estates with forward-thinking clients across sectors.
If you’re ready to evolve from reactive firefighting to proactive, policy-driven operations, we can help. Talk to our team and start shaping your agentic future today.
Agentic AI refers to artificial intelligence systems that act as goal-directed agents — capable of making autonomous decisions within defined policy boundaries. In IT operations, this means systems that can monitor, analyse, decide, and act in real time across infrastructure without waiting for human triggers.
Traditional AIOps typically focus on pattern recognition, anomaly detection, and alert prioritisation. Agentic AI goes further by enabling autonomous execution of remediations, optimisation actions, and even policy adjustments — all while being governed by safe-action protocols and auditable logic.
An AIOps maturity model is a framework used to assess how advanced an organisation is in adopting AI-driven operations. It spans stages from basic observability to full autonomy under policy guardrails. Deimos' four-stage model includes: Visibility, Assistance, Autonomy under Supervision, and Adaptive Agency.
Yes, it can be — provided the implementation includes auditable logs, explainable decision chains, and a “human-in-the-loop” oversight model for high-risk decisions. Deimos incorporates these principles using tools like GitOps, OPA/Kyverno, and cryptographic signing to meet EU AI Act requirements.
Auto-remediation, cost optimisation, patching, incident response, and SLO-driven scaling are the most common early-stage wins. For example:
Start by consolidating observability data, deploying a central policy engine, and identifying a narrow but high-value use case (e.g., auto-remediation of failed deployments). Use a safe-action pipeline and measure impact rigorously. Deimos supports this through our Cloud Assessment and implementation frameworks.
Leading platforms include OpenTelemetry, Kubeflow, LangGraph, OPA, and GitOps tools like ArgoCD and Flux. Cloud-native environments such as AWS, GCP, and Azure provide foundational infrastructure for building these systems.
Deploying autonomous agents without proper governance can lead to unintended system changes, regulatory non-compliance, or service disruptions. This is why a phased, policy-driven approach — supported by observability, version control, and human oversight — is essential.
Yes, if guardrails like policy engines, human oversight, and audit logs are in place. EU regulations mandate these safeguards.
Hyperscalers, fintechs, and SaaS leaders. Deimos is helping regional clients build these capabilities today.
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