The Context Problem: Why AI Agents Fail in Production

The Context Problem: Why AI Agents Fail in Production

ByAlan Knox

AI agents are failing at an alarming rate. By some estimates, more than 80 percent of AI projects never reach production. That’s twice the failure rate of traditional IT projects. Companies are pouring billions into agent deployments, watching impressive demos, and then struggling to understand why everything falls apart when the system hits the real world.

The industry has drawn two conclusions from these failures, and both are wrong.

The first conclusion is that AI agents simply don’t work. The technology isn’t ready. The hype outpaced the reality. Better to wait for the next generation of models before trying again.

The second conclusion is that the failures were implementation errors. The team didn’t have enough data. The integration was flawed. The prompts needed refinement. With better execution, the same approach will succeed.

Both conclusions miss what’s actually happening. The agents aren’t failing because they’re incapable. They’re failing because we’re deploying them incorrectly. We’re granting broad authority without providing the explicit boundaries that make authority safe to exercise.

The real problem is autonomy without context.

What an Agent Actually Is

Before going further, we need to be precise about what we mean by “agent.” The term gets applied to everything from simple chatbots to sophisticated systems that plan and execute multi-step workflows.

For this discussion, an agent is any AI system that can take actions with real-world consequences without requiring human approval for each step. This includes systems that execute code, modify data, send communications, trigger workflows, or make commitments on behalf of an organization. It also includes systems marketed as “copilots” or “assistants” when they’ve been granted execution authority.

The key boundary is execution authority. A system that only produces recommendations isn’t an agent by this definition, no matter how sophisticated its reasoning. A simple rule-following bot with database write access is an agent, because it can take actions that matter.

This distinction matters because the problems we’re seeing aren’t about intelligence or reasoning capability. They’re about what happens when execution authority exists without appropriate constraints.

The Paramedic Model

My son is a paramedic. Watching him work taught me something about autonomy that the AI industry hasn’t learned.

A paramedic has significant autonomy. They make life-and-death decisions in chaotic environments without waiting for permission. They assess situations, choose interventions, administer medications, and adapt to rapidly changing circumstances. The work requires genuine judgment, not rote procedure-following.

But that autonomy exists within an explicit framework. Defined protocols specify what a paramedic can and cannot do in different situations. Clear scope of practice establishes the boundaries of their authority. Mandatory escalation paths exist for situations that exceed their training or authorization. Continuous oversight ensures quality and catches problems before they compound.

No one argues paramedics should have unlimited authority. No one argues they’re useless because their authority is bounded. The boundaries are precisely what make them effective and trustworthy.

The same paramedic who can administer certain medications in the field cannot perform surgery. The same paramedic who has authority in an emergency scene doesn’t have authority to make long-term treatment decisions. The context defines both what they can do and what they should be capable of doing.

This is how autonomy has always worked in high-stakes environments. Execution authority is granted within defined operating envelopes. The envelope specifies what the autonomous actor can decide independently and what requires escalation. The system is designed so that exceeding the envelope triggers human involvement rather than unconstrained action.

AI agents need the same architecture. The question isn’t whether to grant autonomy. The question is how to bound it appropriately.

Context Is the Missing Architecture

When I talk about context, I don’t mean the tokens in a prompt window or the documents retrieved for a response. I mean the explicit information that enables and limits an agent’s authority.

Context answers three questions:

What should happen? This is business context. What are the agent’s objectives? What does success look like? What priorities govern when goals conflict? Without this, an agent optimizes for the wrong outcomes or pursues efficiency in ways that undermine actual business value.

What must not happen? This is policy context. What actions are prohibited? What constraints are absolute? What regulations or rules govern this domain? Without this, an agent takes actions that are technically effective but organizationally unacceptable.

What can happen? This is technical context. What systems exist? What data is available and reliable? What interfaces work and which are fragile? What legacy constraints shape what’s actually possible? Without this, an agent plans actions it cannot execute or relies on data that isn’t trustworthy.

Within these three functional categories, there are specific types of context that agents need:

Business context includes objectives, priorities, and success criteria. Policy context includes rules, regulations, and prohibitions. Organizational context specifies authority, ownership, accountability, and escalation paths. Temporal context defines when authorizations expire and when decisions need review. Technical context encompasses legacy systems, data quality, interfaces, and dependencies.

Security cuts across all of these. Business context can be manipulated through prompt injection. Policy context can be bypassed through jailbreaks. Organizational context can be spoofed through impersonation. Temporal context can be exploited by replaying stale authorizations. Every type of context is also an attack surface.

When context is explicit, an agent knows its operating envelope. When context is missing or inferred, the agent operates on assumptions that may be dangerously wrong.

Why Inference Fails at Scale

You might think sophisticated models can infer the necessary context. They can pick up on organizational norms from training data. They can reason about what policies likely exist. They can make educated guesses about technical constraints.

This works in demos. It fails in production.

Enterprise context is tacit, distributed, and often contradictory. The real policy for handling customer complaints isn’t in the employee handbook. It’s in the accumulated decisions of experienced staff, the unwritten rules that everyone knows but no one documents, the exceptions that happen routinely but officially don’t exist.

Models cannot infer this. They can infer what organizations typically do. They cannot infer what your organization actually does, especially in the edge cases where getting it wrong matters most.

Legacy systems create undocumented constraints. The reason a process works a certain way often traces back to a technical limitation from years ago that no one remembers but everyone works around. An agent inferring optimal workflow will miss these constraints entirely and take actions that break in ways no one anticipated.

Organizational knowledge is also contradictory. Different departments have different understandings of the same process. Official policy conflicts with actual practice. The answer to “how does this work” depends on who you ask. Human employees navigate this through relationships and institutional knowledge. Agents optimizing on one version of the truth collide with the other versions.

Inferred context fails silently. When an agent guesses wrong about policy or authority, it doesn’t flag uncertainty. It proceeds confidently with incorrect assumptions. The failure only becomes visible when damage has already occurred.

The Failure Modes Are Predictable

Once you understand context as the missing architecture, the common failure modes become predictable rather than mysterious.

Boundary violations happen when agents exceed their authority. Without explicit operating envelopes, there’s nothing preventing an agent from escalating its actions beyond what’s appropriate. A customer service agent authorized to issue small refunds issues large ones. A scheduling assistant authorized to move meetings cancels them. The agent does what it’s capable of doing rather than what it’s authorized to do.

Silent errors happen when agents take confident incorrect action. The agent makes a decision based on incomplete or wrong context, executes it successfully, and reports completion. No error is thrown because nothing technically failed. The problem only surfaces later when humans discover the outcome was wrong.

Escalation breakdowns happen when agents don’t know when to ask for help. Without clear thresholds for escalation, agents either escalate everything (making them useless) or escalate nothing (making them dangerous). Calibrating when to act independently and when to involve humans requires explicit context about stakes, confidence levels, and organizational tolerance for error.

Governance failures happen when no one owns the agent’s behavior. The agent takes action, something goes wrong, and the organization discovers there’s no clear accountability. Different departments disagree about who authorized the agent’s scope. No process exists for adjusting authority based on performance. The agent operates in a governance vacuum.

Context decay happens as time passes. The context provided at deployment becomes stale. Policies change. Personnel change. Systems change. The agent continues operating on outdated assumptions, diverging further from organizational reality with each passing week.

These failures aren’t random. They’re structural consequences of deploying execution authority without the explicit boundaries that make authority safe.

What This Looks Like in Practice

Consider a regional insurance company that deployed an agent to handle policyholder interactions. The mandate was broad: answer questions about coverage, process routine claims, schedule adjuster appointments, and escalate complex issues to human reviewers. The agent had access to policy databases, claims history, and scheduling systems.

The demo was impressive. The pilot showed promising results. Leadership approved wider deployment.

Production revealed every failure mode described above.

Boundary violations appeared first. The agent made statements about coverage that sounded like commitments. When policyholders later filed claims based on what the agent told them, the company faced disputes it shouldn’t have created. The agent was capable of discussing coverage in detail. Nothing told it which statements constituted commitments it wasn’t authorized to make.

Silent errors followed. Claims were processed confidently and incorrectly. Documentation that should have triggered adjuster review was accepted as complete. Amounts that should have required approval were paid automatically. The agent reported successful task completion because nothing technically failed. The problems surfaced weeks later in audits.

Escalation breakdowns compounded the damage. The agent couldn’t reliably distinguish routine claims from complex ones. It escalated too little, handling issues that needed human judgment. When it did escalate, the routing was inconsistent. Complex water damage claims went to the wrong queue. Potential fraud indicators were missed.

Governance failures made remediation difficult. When problems emerged, no one owned the agent’s behavior. The IT team owned the deployment. The claims department owned the process. The compliance team owned the policies. Everyone had input. No one had accountability. Adjusting the agent’s authority required negotiations no one had anticipated.

Context decay made everything worse over time. Policy limits changed. Approval thresholds were updated. New claim types emerged. The agent continued operating on its original assumptions, diverging further from how the company actually worked.

The insurance company faced a choice familiar to many organizations: abandon agents entirely or figure out what actually went wrong.

The answer wasn’t that agents don’t work. The answer was that one agent with broad authority couldn’t possibly have the context required for four distinct jobs. Answering coverage questions requires different context than processing claims. Processing claims requires different context than scheduling adjusters. Each job has its own operating envelope. One agent can’t hold them all.

What the transformation to context-bounded deployment actually looks like (how one agent becomes four, how operating envelopes get defined, how governance becomes concrete) is the subject of deeper work. But recognizing that the failure was architectural rather than technological is the first step.

Why Demos Succeed and Production Fails

There’s a pattern that repeats across the industry. A vendor demonstrates impressive agent capabilities. The pilot shows promising results. Leadership approves broader deployment. Production fails.

This isn’t bad luck. The pattern emerges from a fundamental difference between controlled and uncontrolled environments.

Demos work because the environment is controlled. The data is clean. The scenarios are anticipated. The edge cases are excluded. The humans involved understand the system’s limitations and unconsciously compensate for them. The demo proves the technology works under ideal conditions.

Pilots work because the scope is limited. A small team with deep investment monitors closely, catches problems early, and provides the contextual judgment the agent lacks. The pilot proves the technology works with intensive human supervision.

Production fails because the environment is uncontrolled. Real data is messy. Scenarios are unexpected. Edge cases are constant. The humans involved don’t understand the system’s limitations and assume it handles what it doesn’t. The gap between what worked in the pilot and what’s needed in production turns out to be vast.

The Klarna story illustrates this perfectly. The company announced in 2024 that its AI assistant was handling work equivalent to 700 customer service agents. It processed millions of conversations. The numbers looked great. Then customer satisfaction dropped. Service quality declined. Complex issues went unresolved. By mid-2025, Klarna’s CEO admitted they’d “gone too far” and began rehiring human customer service agents.

The AI could handle the routine cases efficiently. But customer service isn’t only routine cases. It’s frustrated customers with complex problems who need empathy and creative solutions. The agent had execution authority without the context to know when it was out of its depth.

Salesforce experienced similar dynamics. After aggressively positioning AI agents as replacements for customer service staff, the company encountered reliability issues in production. Models lost focus when given more than eight instructions. Complex enterprise implementations struggled. One executive reportedly said the company had “over-rotated” on AI at the expense of human expertise.

These aren’t failures of the technology. The models were capable. The failures were failures of deployment architecture. Broad autonomy was granted without the explicit context needed to make that autonomy safe.

The Counterargument

There’s a reasonable objection to everything I’ve said: doesn’t constraining autonomy eliminate the value of agents?

The argument goes like this: The whole point of agents is to handle novel situations and adapt to edge cases. If you constrain them too tightly, you’ve just built expensive automation. The value comes from letting them reason through situations humans haven’t anticipated.

This objection has merit, but it misunderstands the tradeoff.

Broad autonomy occasionally handles edge cases well. Narrow autonomy reliably handles common cases well. In enterprise contexts, reliable handling of common cases is almost always more valuable than occasional handling of edge cases. This is especially true when edge case failures are costly or invisible.

The failure mode of narrow autonomy is that the agent asks for help. The failure mode of broad autonomy is that the agent confidently does the wrong thing. One of these is recoverable.

Constraints can also be dynamic. Context-bound autonomy doesn’t mean static rules. Operating envelopes can expand based on demonstrated reliability. An agent that consistently performs well within tight boundaries earns expanded authority. An agent that struggles has its boundaries tightened. The constraint is that expansion is deliberate, not emergent.

The demos deceive. They show agents handling complex scenarios successfully. They don’t show the failure rate, the edge cases that went wrong, or the intensive human oversight operating behind the scenes. Impressive demos of broad autonomy succeed because demo environments are controlled. Production environments are not.

The Economic Case

There’s a natural assumption that constrained autonomy is more expensive. You have to build the context architecture. You have to design the operating envelopes. You have to maintain governance processes. Wouldn’t it be cheaper to deploy capable models and let them figure things out?

The math runs the other way.

Higher upfront investment in context and governance produces lower total cost when you account for failure recovery, reputation damage, and abandoned deployments. The Klarna reversal wasn’t free. The Salesforce reliability issues had costs. Every production failure requires remediation, often under pressure, often expensive.

Organizations that build context architecture once can deploy agents into it repeatedly. The investment compounds. Organizations that skip context architecture pay for its absence with each deployment.

Narrow autonomy that works is cheaper than broad autonomy that fails. The question isn’t whether you can afford the upfront investment. The question is whether you can afford the alternative.

Humans Own Decisions

There’s a phrase that appears constantly in discussions of AI deployment: “human in the loop.” It’s become almost meaningless through overuse. Every deployment claims to have humans in the loop. Most of those deployments fail anyway.

The problem is that being in the loop is different from owning decisions.

Decision ownership means accountability. When an agent takes action, someone is responsible for the outcome. Not responsible for supervising the agent or approving its outputs, but responsible for the decision itself. If the decision is wrong, someone’s judgment was wrong.

Human-in-the-loop often means approval theater. A human clicks “approve” on recommendations they don’t have time to evaluate. The human presence provides legal cover without providing actual judgment. When something goes wrong, everyone points at everyone else.

Decision ownership requires that humans maintain the context agents operate within. Humans define the operating envelopes. Humans set the escalation thresholds. Humans update policies as circumstances change. Humans audit outcomes to identify context gaps. The ongoing work of context maintenance is human work.

This isn’t a minor reframing. It changes what organizations need to build. Instead of systems where humans supervise agents, you need systems where humans own the decisions agents execute. Instead of training humans to approve agent outputs, you develop humans who can author and maintain the context agents need.

Organizational Readiness

If context is the missing architecture, who builds and maintains it?

This is where many organizations discover they have a capability gap. The skills required to create and maintain agent context aren’t the same as traditional IT skills or traditional business analyst skills. They’re a hybrid that most organizations haven’t developed.

Context literacy means understanding what agents need to operate safely. What information must be explicit? What assumptions are dangerous to leave inferred? What edge cases need specific handling? Most employees have never thought about their work in these terms.

Monitoring skills mean knowing when agents are operating outside their envelopes. Reading decision traces. Interpreting confidence signals. Recognizing when to intervene. These skills don’t exist in most organizations because nothing has required them before.

Context maintenance means keeping authorizations, policies, and technical information current. Context decays. The agent that worked last month operates on stale assumptions this month. Someone needs to own the ongoing work of keeping context accurate.

Governance is the meta-layer that makes everything else work. Who decides what authority an agent receives? Who approves changes to operating envelopes? How are conflicts between business units resolved? Who is accountable when agents act outside their bounds? How is context audited and updated?

These aren’t training problems. You can’t solve them with a workshop. They’re organizational capability problems that require new roles, new processes, and new ways of thinking about work.

Organizations building AI agents need to build the human capabilities to govern them. The investment in organizational readiness is as important as the investment in technology.

Why the Industry Keeps Getting This Wrong

If context-bound autonomy is the pattern that works, why does the industry keep deploying context-free autonomy?

The incentives are misaligned.

Vendors benefit from broad applicability claims. “Our agent can handle any task” is a better pitch than “our agent works well within carefully defined constraints.” Demos showcase capability breadth, not production reliability. Sales cycles reward impressive demonstrations over disciplined deployment.

Buyers face pressure to show AI adoption. Boards ask what the company is doing with AI. Competitors announce agent deployments. The pressure is to deploy something, quickly, visibly. The pressure is not to deploy something carefully, correctly, sustainably.

Internal champions oversell to secure budget and attention. The business case for constrained autonomy with careful governance is less exciting than the business case for transformational automation. By the time the deployment fails, the champion may have moved on.

Influencers reward novelty and boldness. “Agents will replace everything” gets attention. “Agents work when properly constrained” doesn’t trend. The information environment systematically amplifies hype and suppresses nuance.

The result is an ecosystem that pushes organizations toward exactly the deployments that fail.

But the ecosystem is beginning to shift. The high-profile reversals are getting attention. The data on failure rates is becoming harder to ignore. Organizations that tried broad autonomy and failed are becoming more receptive to constrained approaches. There’s a growing recognition that the pattern needs to change.

A Better Vocabulary

Part of the problem is language. The words we use to discuss agents obscure more than they reveal.

When we say “autonomy,” we imply something binary. The agent is autonomous or it isn’t. This framing hides the real question, which is about degree and scope. Autonomy within what boundaries? Autonomy for what decisions? Autonomy with what oversight?

“Operating envelope” is more precise. It makes the boundaries explicit. An envelope has a defined inside and outside. It can be described, measured, adjusted. It invites the question of what’s in scope and what isn’t.

When we say “intelligence,” we focus on model capability. Can the model reason? Can it plan? Can it handle complex situations? This framing directs attention to the technology and away from deployment architecture.

“Context” redirects attention appropriately. The question becomes not how smart the agent is but whether it has the information needed to act safely. A less capable agent with appropriate context outperforms a more capable agent without it.

When we say “collaboration” between humans and AI, we obscure accountability. Who decided? Who is responsible? Collaboration sounds good but hides the question of ownership.

“Decision ownership” makes accountability explicit. Someone owns each decision. The owner is responsible for outcome. The agent may execute, but the human owns.

When we say “training” for workforce readiness, we imply a one-time intervention. Learn the skills, apply the skills, move on. This framing misses the ongoing nature of the work.

“Organizational readiness” captures the broader requirement. Governance, roles, processes, capabilities. Not a training program but a systemic capability to be built and maintained.

These aren’t just semantic preferences. The language shapes how organizations think about what they’re building. Better vocabulary leads to better decisions.

Questions Before Deployment

If you’re considering an agent deployment, here are the questions that matter:

What is the operating envelope? Can you describe precisely what the agent is authorized to do and what it isn’t? If the envelope is vague or undefined, the deployment isn’t ready.

Who owns decisions? When the agent takes action, who is accountable for the outcome? If the answer is “the agent” or “the team” or “everyone,” no one is accountable.

Who maintains context? Who keeps the operating envelope current as circumstances change? If no one is assigned this responsibility, context will decay.

What happens outside the envelope? When the agent encounters something beyond its authority or confidence, what occurs? If the answer is “it does its best,” you have a failure mode waiting to happen.

How will you know if context becomes stale? What signals indicate that the agent’s operating assumptions have diverged from organizational reality? If you don’t have an answer, you won’t catch decay until it causes damage.

What governance exists for changing authority? Who approves expanding or contracting the agent’s scope? What evidence is required? If authority changes informally or by default, you’ve lost control of your deployment.

If you can’t answer these questions clearly, you’re not ready to deploy. The technology may work. Your organization isn’t prepared to use it safely.

The Path Forward

AI agents represent genuine capability. The technology works. The potential value is real. Organizations that deploy agents effectively will gain meaningful advantages in efficiency, responsiveness, and capability.

But effective deployment requires architecture the industry has largely ignored. Autonomy must be bounded by explicit context. Humans must own decisions even when agents execute them. Organizations must build the capabilities to create and maintain context over time. Governance must exist to manage authority deliberately rather than letting it evolve by accident.

This is harder than buying a platform and hoping for the best. It requires upfront investment in context architecture, ongoing investment in maintenance, and organizational change to build new capabilities.

That difficulty is precisely the advantage. Most organizations won’t do this work. They’ll deploy with broad autonomy, experience failures, and either abandon agents or struggle through expensive remediation. The organizations that build context-bound autonomy from the start will deploy agents that actually work while others cycle through failures and rollbacks.

The competitive advantage goes to those willing to treat agent deployment as a capability to build rather than a product to purchase.

The technology is ready. The question is whether your organization is ready to use it correctly.

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