The tangled web of complex business operations should in theory be no match for AI agents. At this landmark point in AI’s storied history, businesses are developing agents that are capable of autonomous reasoning and complex workflow orchestration. But there’s something missing: context. Gaining that operational context is the only way agents can get to grips with end-to-end, business-critical processes and evolve from generic to specific, specialized, strategic action. This post explains why.
Why Enterprise AI agents need context to act reliably
Summary
- AI agents don’t automatically understand the nuances of your business process landscape, which can create operational blind spots, friction, and low-value, generic recommendations and outcomes.
- Context gives Enterprise AI agents the operational facts, relationships, rules, and guardrails they need to execute complex tasks successfully, efficiently, and at scale.
- Celonis provides a dynamic ground truth of how the individual business runs, translated into a language that AI understands, and helps companies embed that operational context into AI agents (whether inside or outside Celonis).
What are AI agents in business?
James Bond – perhaps the best-known agent of them all – has famously used his quick thinking and agility to combat lasers, sharks, and silver-toothed henchmen. AI agents, meanwhile, use their formidable problem-solving and processing skills to tackle foes in the world of business, such as supply chain volatility, demanding customer expectations, and digital transformation challenges.
By reasoning through a task, retrieving information to identify a solution, then coordinating action across tools, systems, and people, AI agent technology can support productivity, efficiency, and some commercial goals. Agents can be used to streamline traditional automation tasks, such as resolving exceptions and routing cases, all the way up to orchestrating multi-step, function-spanning processes.
In consumer-based or simple productivity use cases, agents generally only need task instructions and access to the relevant tools or systems. But in enterprise settings, agents need to thoroughly understand the process they’re acting within, including interdependencies and hand-offs. Otherwise, they risk colliding with other agents or process automations, choosing the wrong next step, or overlooking downstream consequences.
What does “context” mean for AI agents and why do they need it to act reliably?
We’ve all heard the horror stories about AI: the hallucinations, the agent collisions, the operational drift, the outdated action... That’s because prompts, isolated records, and haphazard tool access aren’t enough to fend off AI agents’ equivalent to a Bond villain: operational blind spots. Prompts can’t sufficiently describe the full operational enterprise reality, tool access can leave you vulnerable to token-maxxing, and RAG retrieves information without contextualizing it and translating it into a language that AI understands. Ultimately, AI solutions remain generalists, when businesses need specialists.
Service issues and operational efficiency challenges span systems, teams, and processes, so intelligent agents need a holistic operational view. By combining business knowledge (rules, KPIs, constraints, relationships, ownership, and risks) and process data (process history, current state, and live operational insights), agents gain the contextual information they need for sound operational judgment. Then they can evaluate trade-offs, upstream causes, and downstream impact to establish the most effective action and monitor the outcome.
A supplier delay is a good example. A prompt might contain the supplier name and the revised ETA, but the AI agent also needs to know which purchase orders are affected, which customer orders depend on the material, what inventory is available, what alternatives exist, and what the cost-service-risk trade-off looks like.
In addition to more informed, complete responses, context also prevents “black box” decision-making by pinpointing the data provenance and execution paths, linked to business policies. This traceability gives businesses greater trust and confidence, which is particularly important for AI agents.
- Check out our glossary of all the context AI terms you need to know
How context provides guardrails for reliable AI agents
Context isn’t just about helping AI agents make consistently better decisions, but also about preventing them going rogue once deployed. Guardrails give you peace of mind that, no matter how vast and specialized your fleet of agents, they’ll act within business rules, risk thresholds, compliance requirements, and approval workflows when you send them out into the big wide world of your enterprise landscape.
By grounding AI safeguards in context rather than static guidelines, agents can better navigate the nuances of daily operations. Because different actions carry varying levels of impact, guardrails must be dynamically calibrated. AI applications should be equipped to determine when an action warrants automated execution, when it calls for a recommendation, or when it must escalate to human oversight, adapting based on required confidence levels and approval chains.
For example, a credit-block agent might be fully authorized to recommend releasing a hold for low-risk orders. But strict guardrails still require human approval whenever the agent encounters orders exceeding a predefined financial threshold. Guardrails should also include monitoring and feedback loops, so organizations can assess process performance, adherence, and agent activity over time.
How Celonis AI development helps build reliable agents
At Celonis, we might not be able to equip your AI agents with exploding pens and electromagnetic watches, but we can empower them with operational context through a state-of-the-art build experience.
The Celonis Context Model (CCM) does all the hard work extracting, standardizing, and enriching real-time operational context from your business, which our Intelligence API and Process Intelligence MCP Server make easy for agents to consume. The Celonis Agent Tools MCP asset also provides ready-to-use tools for AI applications to search Celonis data models and knowledge bases, get real-time context including KPIs and process insights, trigger actions in external systems, and write decisions back to Celonis. So even if you’re already building agents in third-party AI platforms, you can still give them a powerful upgrade with operational context.
By infusing AI agent technology with both process intelligence and decision intelligence, the CCM gives them hindsight, insight, and foresight into how your business runs and how it could be improved. And you can use the Platform to define optimal, strategic AI insertion points and guardrails, then monitor solutions continuously.
Premium surfaces manufacturer Cosentino is just one company that’s reaping the rewards. They used Celonis to build and deploy an AI agent to streamline credit-blocked order management. These orders require both automation and judgment, as agents can’t just release every blocked order. With operational context into customer status, credit risk, order value, payment history, policy rules, revenue impact, and approval requirements, Cosentino’s Order Management agent can provide a reliable recommendation to either remove the block or maintain it for further investigation. For Cosentino, developing these custom AI agents with Celonis has been a “game changer”.
This success story shows how operational context helps agents achieve their mission. If you want intelligent agents that can prioritize, recommend, improve, and automate workflows with greater reliability, the name’s context, operational context.
Watch our short, 11-minute demo to find out why the journey to AI ROI starts with your processes.