Agentic AI & Real-Time Automation Platforms

From AI answers to AI actions — safely, governed, and in real time.

Many organizations already use AI to generate text. Very few can safely let AI take action.

Agentic AI is about autonomous or semi-autonomous systems that observe events, reason over context, decide next steps, interact with tools and systems, and execute actions — safely and auditable.

Acosom designs and implements enterprise-grade agentic AI platforms that integrate AI agents with real-time systems, streaming data, and internal tools — without losing control.

What Your Organization Gains

Move beyond chatbots to AI systems that can act autonomously, safely, and in real time.

AI That Can Act — Not Just Respond

Move beyond chatbots. Your AI systems can trigger workflows, update systems, enrich data, and respond to events automatically.

Controlled & Auditable AI Actions

Every agent action is logged, validated, and governed. No uncontrolled tool usage. No “black box” automation.

Real-Time Decision Making

Agents react to live data streams, not static prompts. This enables use cases impossible with request/response LLMs alone.

Safe Integration with Enterprise Systems

Agents interact with internal APIs, databases, ticketing systems, monitoring tools, and IoT platforms — under strict guardrails.

Deterministic, Repeatable Behavior

We design agents to behave predictably using structured reasoning, state management, and bounded autonomy.

A Scalable Automation Foundation

Agent logic becomes reusable across departments, not a one-off experiment.

Success Story

From Incident Detection to Automated Resolution

A financial services client needed to reduce incident response times across their infrastructure. We implemented an event-driven agent system that monitors Kafka streams, correlates alerts, and automatically remediates common issues.

Result: 60% reduction in MTTR, 40% fewer escalations to on-call teams, and complete audit trails for compliance. The system safely handles thousands of events daily.

Discuss Your Use Case

What We Build

Agentic AI Architectures

Agents are software systems, not demos.

We design agent systems with planning & reasoning components, short- and long-term memory, task decomposition, retry & failure handling, and escalation paths to humans.

Core components: Goal interpretation, context assembly, action planning, state persistence, feedback loops, and human-in-the-loop integration. Each component is designed to be debuggable, testable, and auditable.

Event-Driven & Real-Time Agents

Beyond prompt-based agents.

We build event-driven agents that react to Kafka topics, Flink streams, database change events, monitoring alerts, IoT signals, and business events.

Technologies: Flink Agents Framework, Akka Agents, custom event-driven agent runtimes. Agents operate continuously and reliably.

MCP (Model Context Protocol) Servers

The missing link between LLMs and enterprise systems.

We build custom MCP servers that expose internal tools safely to AI agents, enforce schemas and contracts, validate inputs and outputs, apply authorization & rate limits, and log and audit every call.

MCP-enabled tools: Internal APIs, databases, monitoring systems, ticketing platforms, workflow engines, operational systems. This is how AI becomes operationally safe.

Tooling & Workflow Integration

Agents become part of your operational fabric.

We integrate agents with CI/CD systems, ITSM tools, ERP/CRM systems, monitoring & observability stacks, data platforms, and real-time analytics systems.

Agent Governance & Risk Control

Essential for regulated environments.

We implement governance patterns including bounded autonomy levels, approval workflows, kill switches, action simulation/dry runs, policy enforcement, and escalation to humans.

Custom Agent Interfaces

Agents are transparent — not mysterious.

Depending on the use case, we provide chat-based UIs, dashboards showing agent decisions, approval interfaces, logs & audit views, and integration into existing portals.

Technologies We Use (Vendor-Neutral)

Agent Frameworks & Runtimes

Model-agnostic by design.

LangChain (where appropriate), custom agent runtimes, Flink Agents Framework, Akka Agents, and MCP (Model Context Protocol).

LLMs can be on-prem, hybrid, or cloud-based.

Event Streaming & Integration

Real-time data foundation.

Kafka & event streaming, REST/gRPC/async APIs, database change streams, and message queues.

Governance & Observability

Production-grade agent systems.

Policy engines, observability & tracing tools, audit logging, and monitoring dashboards.

Why Choose Acosom

What makes agentic AI different from chatbots?

Chatbots respond to user prompts. Agentic AI systems observe, reason, and act autonomously. They monitor event streams, make decisions based on real-time context, interact with enterprise systems through tools, and execute actions with proper governance.

The key difference: Agents can operate continuously without human prompts, making them suitable for automation scenarios that require proactive action.

How do you ensure agents don't take dangerous actions?

We implement multiple safety layers:

Bounded autonomy: Agents have strictly defined permissions and capabilities
Approval workflows: Critical actions require human approval
Dry run mode: Test agent behavior without actual execution
Kill switches: Immediate shutdown capability
Validation layers: Input/output checking before any action
Audit trails: Complete logging of all agent decisions and actions

Agents are designed to be conservative and escalate uncertain cases to humans.

Can agents work with our existing systems?

Yes. We build MCP servers and custom integrations that expose your existing systems to agents through well-defined interfaces. This works with virtually any system that has an API: databases, ITSM tools, monitoring platforms, workflow engines, CRM/ERP systems, and operational tools.

Vendor-neutral approach: We integrate with your existing stack rather than requiring replacement.

What's the difference between event-driven and prompt-based agents?

Prompt-based agents wait for user input, process a request, and respond. They’re stateless and reactive.

Event-driven agents continuously monitor data streams (Kafka, Flink, database changes, monitoring alerts) and react to events automatically. They maintain state, can handle complex workflows, and operate without constant human input.

Use event-driven agents when: You need proactive automation, real-time response to system events, or continuous monitoring and action.

How long does it take to deploy an agentic AI system?

A production-ready agent system typically takes 8-16 weeks:

Weeks 1-3: Use case definition, architecture design, safety requirements
Weeks 4-6: Agent design, MCP server development, integration setup
Weeks 7-10: Testing, validation, dry-run scenarios
Weeks 11-16: Production deployment, monitoring setup, governance implementation

Proof-of-concept demonstrations for specific use cases are possible in 2-3 weeks.

Do we need our own LLM infrastructure to use agentic AI?

No. Agentic AI systems are model-agnostic. You can use:

On-premises LLMs (if you have private AI infrastructure)
Hybrid setups (some agents on-prem, others cloud-based)
Cloud-based LLM APIs (with appropriate data controls)

Our recommendation: For highly sensitive use cases, combine on-premises LLMs with our agentic platforms. For others, cloud-based LLMs work fine when properly governed.

Ready to build safe, governed AI agents that take action in real time? Let’s talk!