Large language models (LLMs) can reason, plan, and write impressively well—until they run out of room. Every model has a fixed “context window”, meaning it can only pay attention to a limited amount of text at one time. In real workflows—project planning, multi-step problem solving, long customer conversations, or tool-driven agents—information quickly accumulates and older details get pushed out. The result is context overflow: the model forgets critical constraints, repeats work, or makes inconsistent decisions. This is where agentic AI training becomes highly relevant, because agents must manage memory and state over many steps without losing accuracy.
This article explains why context overflow happens and how summarisation, compression, and selective retrieval can keep planning and reasoning stable over long horizons.
1) Why Context Windows Overflow During Planning
Context overflow is not just about “too many tokens.” It’s about unstructured growth. Planning and reasoning generate large traces: intermediate steps, alternative options, tool outputs, notes, and corrections. If everything is kept, the context becomes noisy. Important constraints (budget limits, deadlines, user preferences, definitions) may be diluted by low-value text (repeated explanations, verbose tool logs, or irrelevant side trails).
Common failure patterns include:
- Constraint drift: earlier requirements drop out, and the plan subtly changes.
- Looping: the model re-derives the same steps because prior decisions are no longer visible.
- Overconfidence: the model fills gaps with guesses when evidence was in earlier context.
- Misaligned actions: the agent calls tools using outdated parameters or assumptions.
Solving this requires a disciplined approach: keep only what the model needs to be correct and consistent, and reintroduce it at the moment it matters.
2) Summarisation That Preserves Decision-Critical Details
Summarisation is the first line of defence, but it must be designed for reasoning—not just brevity. A good operational summary is structured and anchored to decisions. Instead of compressing everything into a paragraph, summarise into stable fields that can be referenced repeatedly.
Effective summarisation patterns:
- Rolling summary (session memory): after each major step, update a short “current state” note containing goals, constraints, decisions made, and open questions. This prevents the agent from re-reading the entire conversation every time.
- Hierarchical summaries: keep multiple layers—micro-summaries of recent steps, and a macro-summary of the overall objective and key constraints. When the context gets tight, discard raw text first, then micro-summaries, and retain the macro layer longest.
- Decision logs: maintain a compact list of choices and rationale (e.g., “Chose option B because A violates latency constraint”). This is especially useful in agentic AI training, where reproducibility and consistent behaviour matter.
- Actionable TODO list: capture next actions with owners, dependencies, and acceptance criteria. This protects the plan’s continuity even if earlier discussion is trimmed.
The key principle: summarise state and intent, not just narrative. State tells the model what is true; intent tells it what to do next.
3) Compression Techniques Beyond Plain Summaries
Sometimes summarisation alone is not enough, especially when you need to retain many specifics (requirements, tables, test results). Compression aims to store more information in fewer tokens while keeping it usable.
Practical compression strategies include:
- Schema-based compression: convert verbose text into compact formats like key-value pairs, checklists, or JSON-like structures (even if you keep it human-readable). For example: “Constraints: {budget: 2L, timeline: 6 weeks, channels: email+WhatsApp}”.
- Entity and constraint extraction: store only named entities, parameters, and rules. Planning failures often come from losing a single number, date, or definition.
- Redundancy removal: eliminate repeated explanations and keep only the final agreed wording. This reduces noise and improves retrieval accuracy later.
- Chunking with titles: break long context into labelled chunks (“User Goals”, “Technical Constraints”, “Decisions”, “Tool Outputs”). Labels act like signposts so the model can navigate quickly.
Compression should be loss-aware. Decide what can be safely dropped (small talk, duplicated text) versus what must remain (constraints, definitions, commitments).
4) Selective Retrieval: Bring Back Only What You Need, When You Need It
Selective retrieval prevents overload by keeping most content outside the active context and pulling it in only when relevant. This is the core idea behind retrieval-augmented workflows: store long-term information externally and fetch the right pieces for the current step.
What makes retrieval work well:
- Relevance-first querying: retrieve based on the current task (“Find constraints about timeline and scope”), not general similarity alone.
- Salience rules: prioritise constraints, user preferences, and decisions over background discussion. If two items conflict, prefer the most recent validated decision.
- Recency + importance balancing: new information is not always better. Retrieval should surface older constraints if they govern the plan.
- Tool-state retrieval: if an agent used external tools, store tool outputs separately and retrieve only the specific results needed for the next action.
In agentic AI training, retrieval policies are often as important as the model itself. A strong agent is not the one that remembers everything; it is the one that retrieves the right evidence at the right time.
Conclusion
Context window limitations are a practical constraint in real-world planning and reasoning. The solution is not to stuff more text into the prompt, but to manage information intentionally. Use structured summarisation to preserve state and decisions, apply compression to keep essential details dense and clean, and rely on selective retrieval to reintroduce evidence only when it is relevant. When these techniques are combined, long workflows become more consistent, less repetitive, and far more reliable—exactly the stability expected from modern agentic AI training systems.