Opening — Why This Matters Now

The age of static AI agents is quietly ending. As enterprise workflows lean on increasingly autonomous systems, the industry is discovering an uncomfortable truth: most agents think the same way today as they did yesterday. They don’t learn from their mistakes. They don’t optimize their internal logic. They don’t decide when to use more (or less) compute.

Experience-Guided Reasoner (EGUR), the framework examined in this article, pushes back against that rigidity. It proposes something deceptively simple yet strategically profound: let the system redesign its own reasoning strategy at inference time, based on the lessons accumulated from previous tasks.

In business terms: imagine software that not only responds to queries, but also refactors its own processes, lowering cost and increasing accuracy with each interaction. That’s not just agentic—it’s operationally compounding.

Background — The Limits of One-Size-Fits-All Reasoning

Traditional AI agents typically fall into two camps:

1. Static architectures: Predefined workflows (CoT, code interpreters, evaluator loops) that never evolve once deployed.
2. Prompt-steered agents: Systems that append “memory” to inputs, nudging the model but never altering the underlying strategy.

Both approaches suffer from structural inflexibility. You cannot, for example:

• Switch from an agentic loop to a single-shot workflow.
• Disable tools when they harm performance.
• Change sampling parameters.
• Cache full computational procedures for reuse.

In practice, this leads to classic enterprise frustrations:

• Systems overthink simple tasks.
• They under-think complex ones.
• Costs scale unpredictably.
• Mistakes repeat themselves indefinitely.

EGUR’s contribution is to elevate adaptation from prompt-level to strategy-level. Instead of nudging the agent, you give it authority over its own computational design.

Analysis — What EGUR Actually Does

EGUR is powered by two components:

1. The Guide — A Meta-Strategist

Instead of executing a fixed procedure, the system first generates multiple candidate strategies—complete, runnable workflows specifying:

• LLM calls
• Tools to use or disable
• Sampling parameters
• Control flow structure
• Recursion or parallelization patterns

Think of the Guide as a chief architect that knows the history of past performance and drafts the most promising plan for the current task.

2. The Consolidator — A Memory Curator

After executing strategies, the system collects:

• The answer
• Execution traces
• Cost
• Verifier feedback

The Consolidator updates a structured memory, storing:

• Winning strategies for specific task types
• General heuristics (e.g., “use CoT for subjective reasoning; CodeAct harms object counting”)
• Failure patterns
• Cost-heavy behaviors to avoid

This yields a living repository of practical, experience-driven insights.

How These Interact

The workflow looks like this:

1. Generate strategies.
2. Run them.
3. Evaluate them.
4. Update memory.
5. Next query benefits from everything that happened before.

In effect, EGUR turns inference into a lightweight, continuous optimization cycle.

Findings — Accuracy Up, Cost Down

Across AIME, 3-SAT, and BBEH tasks, EGUR consistently outperforms both static strategies and memory-augmented baselines.

Below is a simplified view of the trade-off:

And a visual summary of how EGUR shifts the performance frontier:

Accuracy↑ | EGUR 90% | . · | . . | . 70% |——————————- Cost → | Traditional Prompt-memory | agents systems |

EGUR is not merely more accurate; it learns when not to waste compute. For example, it:

• Removes the code interpreter entirely for object counting.
• Reduces parallel sampling when unnecessary.
• Switches to simple workflows for subjective evaluations.

Implications — What This Means for Business & Automation

1. Enterprise AI stacks become self-optimizing

Instead of tuning workflows manually, companies gain systems that improve with usage.

2. Cost curves flatten rather than scale

EGUR learns which tasks require heavy agentic behavior and which do not. Expect:

• Lower token consumption
• More predictable execution costs
• Better alignment between task complexity and computational investment

3. Governance frameworks can track reasoning evolution

Since EGUR makes its strategies explicit and inspectable, compliance teams can:

• Audit control flows
• Monitor tool usage
• Evaluate decision boundaries

This is a substantial step forward in machine assurance.

4. Automated process design becomes feasible

EGUR blurs the line between agent and automation engineer. The system effectively redesigns its own workflow—something enterprises currently pay consultants to do.

Conclusion

EGUR reframes an important question: What if AI didn’t just reason better—but reasoned better about how to reason?

By letting models dynamically generate, evaluate, and refine their own strategies, EGUR introduces architectural adaptability that has been missing from current agent systems.

For businesses, it signals a shift from static automation to compounding, self-optimizing intelligence—the kind that gets cheaper, faster, and more accurate the longer it runs.

Cognaptus: Automate the Present, Incubate the Future.