8 min readOct 12, 2025

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Evolution of AI Agents

Stateless Agents

• Agents that don’t remember past interactions. Every user request is handled fresh, without persistent memory.
• Any history or “context” must be re-supplied with each call (via prompt, recent messages, etc.) because the agent itself holds no internal state.

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Strengths & When They’re Good

• Fast and simple less infrastructure, fewer moving parts. Easier to build and deploy. (Daffodil Software)
• **Scalable **without state to track, it’s easier to scale horizontally or use serverless setups.
• **Predictable behaviour **for a given prompt + tools, output is more stable fewer surprises from forgotten history.
• Works best when tasks are short, isolated, or “single-turn” (summarization, classification, translation, simple Q&A). (The O3 AI Stack Platform)

🧱 Technical Failure Points

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1. **No long-term memory of user interactions: **The system lacks a memory backend that can track evolving user preferences, corrections, or decisions across time. There’s no way to semantically index past interactions and retrieve relevant insights.
2. **Cold start on every session: **Since there’s no persistent state, the agent reprocesses each user as if they’re new. It cannot recall prior conversations, past decisions, or previous errors — leading to repetitive and shallow interactions.
3. **Prompt-based profile injection is unsustainable: **Developers often resort to hardcoding user traits into system prompts (e.g., “The user is vegan and works in finance”). But this quickly becomes unscalable and token-inefficient. The moment the profile changes, the prompt must be manually updated or regenerated.
4. Multi-session tasks are untrackable: Agents can’t handle tasks that span across sessions — like planning a multi-day trip, tracking a workout regime, or following up on an unresolved issue. Without a memory graph or task timeline, everything resets the moment the session ends.

2. Stateful, Context-Aware Agents

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What are Stateful AI Agents?

Stateful AI agents are systems designed to retain and use contextual information from past interactions. They maintain a form of memory, either within a session or persistently across sessions, which allows them to understand ongoing tasks, recognize user preferences, and build on previous exchanges to deliver more intelligent and personalized responses.

Characteristics of stateful AI agents:

• These agents can recall prior inputs, user history, or task progress, allowing them to respond more naturally and maintain coherent conversations.
• Because they remember user preferences, behavior, or goals, stateful agents can tailor their responses to individual needs.
• They often involve more advanced sessions or memory management, which increases design and implementation complexity.
• Stateful agents can dynamically adjust their behavior based on new information, feedback, or a shift in user intent.

Limitations

• **Complex **memory stores, retrieval systems, context management, summarization, etc. (Daffodil Software)
• **Cost & performance overhead **storing, retrieving, processing past context adds latency and computational cost. (hypermode.com)
• Memory drift / context corruption old or irrelevant info may mislead; sometimes memory becomes noisy or inconsistent. (letta.com)
• Privacy & data risks storing personal or historical data raises security, regulatory, and ethical concerns. (kognition.info)

Key Differences Between Stateless and Stateful AI Agents

Stateless AI Agents Vs Stateful AI Agents

Planners

• Agents that can break down a goal into multiple steps, sequence them, use tools or APIs, and execute over time.
• They don’t just react to a single input they plan ahead, decide on subgoals, sometimes revise the plan, and orchestrate execution.
• Planning may involve reasoning, symbolic logic, or hybrid neuro-symbolic systems.

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Key Strengths

• Can handle complex, multi-step tasks such as workflows, projects, tasks that require dependencies, tool usage, etc.
• Better task completion planning helps avoid dead ends, missed steps, or inefficient execution.
• **Adaptivity **when combined with feedback, planners can revise plans on the fly (if something goes wrong) instead of failing entirely.
• **Efficiency in resource/tool usage **by organizing actions, reducing redundancy, using tools strategically.

Challenges & Drawbacks

• Error compounding small mistakes early in the plan can cascade; the more steps, the more room for failure. (Even a low error per step adds up.) (Business Insider)
• Tool misuse / infeasible plans the planner might propose a plan that uses tools wrongly or assumes things that aren’t true of the environment. (arXiv)
• **Cost & latency **more reasoning, more calls, possibly longer latency. Planning ≠ free.
• **Plan rigidity vs dynamism **if the plan isn’t adaptable, then unexpected changes (environment shifts, missing info) break the plan.

How to Address Shortcomings

• **Plan verification / evaluation loops **after generating a plan, check whether steps are feasible, tools available, make estimates, maybe simulate.
• **Dynamic re-planning **agent monitors execution, and if a step fails or something changes, it updates or replans. (Some systems trigger re-planning when failure occurs or after certain steps.) (embodied-ai.org)
• Hierarchical decomposition break big goals into subgoals, sometimes with low-level planners for details, high-level planners for strategy.
• Use of neuro-symbolic hybrids combine symbolic planners (for rigor, constraints) with LLM + reasoning (for flexibility, natural language understanding). (arXiv)

Learners

• Agents that improve over time by learning from feedback or new data, not just following fixed prompt patterns.
• They adapt: online learning, fine-tuning, meta-learning, reinforcement learning from human feedback (RLHF), etc.
• They try to generalize what they’ve learned so far so new tasks or environments are handled better.

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Key Strengths

• **Better adaptation **they can adjust to new tasks, domains, or preferences without being entirely re-built.
• Continual improvement performance can keep rising as more data feedback arrives.
• More robustness learner agents can recognize failure or weakness and update to correct them.
• **Generalization **meta-learning helps with few-shot or zero-shot tasks, transferring knowledge across different but related tasks.

Where They Struggle

• **Catastrophic forgetting **when learning new tasks, the agent may lose performance on older tasks. (ar5iv)
• Instability — frequent updates, wrong feedback, or noisy training data can cause oscillations or degrade performance.
• Resource / Computation Costs fine-tuning, continual learning, meta-training all need compute; maintaining memory buffers or replay systems adds overhead.
• **Overfitting / Negative Transfer **learning from limited, biased, or wrong examples may harm generalization

Mitigations & Techniques

1. Reflexion is an architecture designed to learn through verbal feedback and self-reflection. The agent explicitly critiques its responses for tasks to generate a higher quality final response, at the expense of longer execution time. Reflexion

**2. Regularization of parameter changes **methods like Elastic Weight Consolidation (EWC), constraining updates so key learned weights are preserved. (seo.goover.ai)

3. Meta-learning frameworks learning good initializations that adapt well to new tasks and are robust to forgetting. (E.g. MAML, La-MAML) (arXiv)

4. Selective forgetting / attenuation of irrelevant memory or parameters to reduce interference. (CVF Open Access)

5. Orchestrators

Agents (or a layer) whose job is coordination, they manage multiple specialised agents (or tools), delegate tasks, ensure consistency, handle hand-offs, monitor progress, and deal with conflicts.

• Think of them like conductors in an orchestra: they don’t perform every note, but ensure all performers work together in harmony toward a big goal.

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Types of AI agent orchestration

There are several types of AI agent orchestration. Real-world systems often combine multiple orchestration styles for more effective results.

Centralized orchestration: A single AI orchestrator agent acts as the “brain” of the system, directing all the other agents, assigning tasks and making final decisions. This structured approach helps ensure consistency, control and predictable workflows.

Decentralized orchestration: This model shifts away from a single, controlling entity, allowing MAS to function through direct communication and collaboration. Agents make independent decisions or reach a consensus as a group. This makes the system more scalable and resilient since no single failure can bring it down.

Hierarchical orchestration: Here, AI agents are arranged in layers, resembling a tiered command structure. Higher-level orchestrator agents oversee and manage lower-level agents, striking a balance between strategic control and task-specific execution. This allows for more organized workflows while still enabling specialized agents to operate with some autonomy. If the hierarchy becomes too rigid, adaptability can suffer.

Federated orchestration: This approach focuses on collaboration between independent AI agents or separate organizations, allowing them to work together without fully sharing data or relinquishing control over their individual systems. This is especially useful in situations where privacy, security or regulatory constraints prevent unrestricted data sharing, such as in healthcare, banking or cross-company collaborations.

Benefits of Multi-Agent Systems

Multi-agent systems (MAS) offer a range of unique benefits by enabling intelligent agents to collaborate, adapt, and operate autonomously in complex, dynamic environments.

• These benefits make MAS an increasingly valuable technology across industries that require efficiency, flexibility, and resilience.

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Potential Pitfalls

• Added complexity of architecture: More components, more moving parts (communication between agents, shared state, monitoring) mean more things can go wrong.
• Latency / resource overhead: Coordination introduces delays, especially when many agents are involved, or hand-off cycles are long. Also overhead of monitoring, error handling, data routing.
• Conflict, consistency, and state management issues: Agents may produce conflicting outputs; keeping a common state or shared context can be hard. Messy state, stale data, synchronization issues.
• Cost / overhead in design, maintenance, and debugging: Observability, testing, validation, system failures are harder to trace; scaling can introduce emergent behavior that wasn’t foreseen.
• Ethical, security and trust issues: Since orchestrators often have elevated control (assigning tasks, routing data), there are risks: misuse, data leaks, biases, or when an orchestrator becomes a bottleneck or attack vector.

References:

1. Daffodial: Stateful vs Stateless AI Agents: Know Key Differences
2. Mem0: Why Stateless Agents Fail at Personalization

Planner Research Highlights

• LLM-Planner: generates high-level plans (subgoals) then grounded execution; supports re-planning when environment or status changes. (embodied-ai.org)
• Modular Agentic Planner (MAP): separates modules (task decomposition, state prediction, evaluation etc.) to improve planning over multiple domains. (arXiv)
• DEPS (“Describe, Explain, Plan, Select”): Builds a plan, but also includes mechanisms for explaining failures and selecting among possible subgoals to refine plans. (arXiv)

Learners Research Highlights

• “Meta-Lifelong Learning with Limited Memory” shows that with small memory buffers and good initialization, models can learn new tasks while retaining older ones. (ACL Anthology)
• “Learning to Forget for Meta-Learning” introduces selective forgetting to improve adaptability. (CVF Open Access)

Orchestrators

• IBM: What is AI agent orchestration?
• Domo.com: What Is Multi-Agent Orchestration? What It Is, Benefits, Examples