Why Context, Not Prompts, Determines AI Agent Performance
Enterprises fixate on prompts, but agent performance depends on execution context. Learn why persistent context—not prompting—determines scalable AI automation.
Prompt engineering improves single responses, but agent performance is determined by how execution context is captured, replayed, and constrained over time.
Prompts operate at the moment of generation, but agents execute across sequences where every decision depends on accumulated state.
As context grows, cost, latency, and reliability hinge on what prior information is re-ingested, cached, or invalidated during execution.
Without persistent decision context, agents repeat failures, lose constraints, and behave inconsistently as workflows extend.
For the past few years, enterprises have obsessed over prompts, with entire roles emerging around their design and an ecosystem of tooling and templates following close behind. This focus delivered early gains because it allowed teams to rapidly improve outputs without modifying the surrounding system. Over time, those gains flattened.
They flattened because prompt engineering operates entirely at the point of generation. Prompts shape how a model responds in the moment, but they do not persist state, govern execution, or record what happened before. As prompts stabilized into reusable patterns, further iteration stopped affecting how systems behaved once tasks extended beyond a single interaction.
Agent-based architectures expose these limitations immediately.
From Text to Execution State
Once systems move beyond single-turn interactions, context stops being optional. It becomes the mechanism by which decisions compound.
Prompt engineering works when each response can be treated as an isolated generation problem. For example, chat interfaces compress context into a narrow, transient exchange. Prior turns influence the next response, but they are not treated as a durable execution state.
Agent systems remove that assumption by introducing continuity: every decision depends on what has already happened. Instead of producing a single response, an agent executes a sequence of decisions over time, each conditioned on an accumulating record of prior actions. Each cycle appends state—inputs gathered across systems, actions taken, tools invoked, observations returned, failures encountered, and approvals granted. That accumulated state conditions for the next decision, and the next. Context grows monotonically and must be reprocessed on every step.
This produces structural effects:
Most cost comes from rereading context. At each step, the model has to read everything that happened before. As the history grows, rereading it costs more than generating the next action.
Small context changes slow everything down. Cached computation only helps if earlier context stays the same. When it changes, the model recomputes from scratch, increasing response time as execution continues.
Early decisions fade unless reinforced. As more state accumulates, initial goals and constraints compete with newer information. If they are not carried forward, they lose influence.
Mistakes repeat when failures are erased. Removing failed attempts also removes the reason they failed. When similar situations occur, the system has no record of what not to do.
These behaviors do not depend on a specific model or vendor. They follow from how transformer-based systems handle growing context during multi-step execution. Stronger models make fewer mistakes, but they still have to reread and reason over accumulated state.
Once agents operate across multiple steps, system behavior depends less on the quality of any single response and more on what information is carried forward between decisions.
How Context Engineering Works During Execution
Context engineering is not prompt optimization under a new name. It concerns how execution state is assembled, replayed, and constrained as an agent runs. The practical questions are straightforward: how much prior state is re-ingested at each step, which parts remain stable, and which changes force the system to recompute everything that came before.
In agent systems, most runtime cost comes from ingesting prior context rather than producing new output. Cache reuse depends on stable prefixes, deterministic serialization, and append-only histories. When earlier parts of the context change—because a timestamp is updated, keys are reordered, or prior actions are rewritten—cached segments are invalidated. The model must reprocess the entire prefix, increasing both latency and cost. These effects are not subtle; they are visible in production traces as execution length grows.
Control problems follow a similar pattern. As agents gain access to larger tool surfaces, action selection becomes less reliable unless it is explicitly constrained. Allowing the model to freely choose among dozens or hundreds of tools increases the likelihood of invalid or inefficient actions. Dynamically adding or removing tools mid-execution compounds the problem by changing the meaning of earlier steps and invalidating cached context. Constraining which actions are available at a given point—without rewriting prior state—produces more stable behavior because the model reasons over a consistent execution history.
These concerns rarely appear at the interface layer. They surface in the execution path, where context is constructed token by token and replayed step by step. That is why they are often missed in discussions that focus on prompts, UX, or model capability rather than how systems actually run.
Why Automation Breaks in the Enterprise
Enterprise IT operations make the execution gap visible.
Observability platforms surface conditions, and automation platforms execute actions, but little connects the two. Signals arrive without enough situational context, and automation runs without visibility into the broader state that would explain whether an action is appropriate, risky, or redundant. Human operators compensate by stitching context together across systems, recalling similar incidents, and applying judgment at the moment of execution.
That judgment is rarely preserved. Most enterprise systems record what action was taken, but not why it was taken.
They capture that an incident was escalated, a service restarted, or a discount approved, but not the conditions, tradeoffs, or exceptions that led to that choice. The reasoning lives briefly in tickets, Slack threads, and escalation calls, then disappears once execution completes.
Without that history, automation cannot build on prior decisions. Each action is treated as a one-off, disconnected from similar cases that came before. There is no accumulated precedent to guide future behavior.
Agents struggle in this environment for the same reason humans compensate for it: the system has no durable record of how decisions were made.
Context Graphs as the Missing System of Record
The absence of decision memory creates a clear requirement: some layer in the system has to capture how context turned into action.
Systems that place agents directly in the execution path meet that requirement by default. At decision time, they see the full surface area involved in execution: which inputs were pulled from which systems, which policies were evaluated, where exceptions were applied, who approved the deviation, and what action was ultimately taken. None of this is inferred after the fact. It is present at commit time.
When that execution trace is persisted, it produces something most enterprises lack: a queryable record of decision lineage. Not just the final state, but the sequence of conditions and judgments that led there.
Over time, these records accumulate into a context graph. This is not a model’s internal reasoning or chain-of-thought. It is an external structure that links the entities the business already cares about—accounts, incidents, policies, approvers, agent runs—through the decisions that connected them. The graph captures what happened in each case and the constraints under which it was allowed to happen.
That structure is what allows autonomy to compound. Without it, systems repeat decisions in isolation. With it, prior decisions become accessible context rather than lost precedent.
What Changes When Context Persists
Taken together, these constraints explain why progress in enterprise AI has slowed despite rapid improvements in model capability. As systems move from isolated interactions to sustained execution, the limiting factor shifts away from generation quality and toward how execution state is handled over time.
Prompt engineering improved interaction because interaction was the unit of work. Agent systems change that unit. Decisions compound, context accumulates, and prior actions shape what is possible next. In that setting, performance, cost, and reliability depend on what information is carried forward, how consistently it is replayed, and whether prior decisions remain accessible as context rather than disappearing after execution.
This is where context engineering becomes decisive. Not as a refinement of prompting, but as an execution concern: how state is captured, constrained, reused, and audited as agents operate across steps. Systems that treat context as ephemeral struggle to generalize. Systems that persist decision context begin to accumulate precedent.
That distinction determines whether automation remains brittle or becomes adaptive. It also marks the boundary between experimentation and production. As agent-based systems take on longer-running workflows, the platforms that matter will not be those that generate the best responses in isolation, but those that retain and apply the reasoning behind prior actions.
Enterprise AI is not stalling because models have stopped improving. It is stalling because execution has become the problem to solve.
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Margo Poda leads content strategy for Edwin AI at LogicMonitor. With a background in both enterprise tech and AI startups, she focuses on making complex topics clear, relevant, and worth reading—especially in a space where too much content sounds the same. She’s not here to hype AI; she’s here to help people understand what it can actually do.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
