How to Improve Agent Performance with Knowledge Management

Sad but true. The closer the agent gets to production, the more the cracks begin to show. Teams make two mistakes at this point. They either look for a bigger/smarter LLM or endlessly iterate on prompting and RAG. Neither works. Successful agents start with the workflow, not the LLM. The more detailed the description of the workflow and outcomes, the less the agent needs to rely on AI. Every time the agent must guess what the next step is or what tools and information to use at this step, it creates an opportunity for small mistakes. They compound across multiple steps into much larger failures. Next, the workflow and the domain expertise required to deliver the outcome must be built into a knowledge graph. Trying to stuff everything into markdown files is a recipe for hallucination pie. The longer the file, the harder it is for LLMs to keep things straight. They lose focus and lose sight of what information is important. Knowledge graphs fix this by giving the agent exactly the information it needs at exactly the step it needs it. When agents get lost, and uncertainty metrics rise, the knowledge graph can deliver examples and metrics that define success and refocus the agent on iterating until it builds an acceptable output. Knowledge graphs can deliver guardrails that prevent agents from falling into endless loops. The goal is to build agents that rely on LLMs as little as possible and only deploy LLMs for what they are good at. Use the smallest models possible, and open-source models should handle over 80% of the workflow. Finally, agents need real-world feedback to improve. Version 1 is never perfect, and it takes multiple improvement cycles to be ready for deployment. Agents and knowledge graphs must be architected to benefit from improvement cycles. Every mistake creates the data required to ensure it never happens again.

I've reviewed 100s of AI agents, and one issue keeps popping up: Agents often lack context about the real world and their own internal logic. Two easy fixes can vastly improve how your agents perform: ☑ Always include real-world context in your prompts. Agents don't inherently understand the current time or location. Give them context explicitly: "current time," "user's location," or what's considered "recent" information. Example: If your agent uses a search tool, include timestamps so it knows what "recent" actually means. ☑ Make your internal logic transparent to the agent. Many developers embed critical business logic directly into their code, invisible to the agent. Clearly outline your logic within prompts so agents understand how information is processed and communicated. This transparency reduces friction and makes user interactions smoother and more effective. Great agents aren't just smarter—they're context-aware. Always remember these tiny improvements—they matter more than you think. I'm Shrey & I share daily guides on AI. If this helped you think about agents differently, click ♻️ reshare to help someone else build better agents.

The places where AI Agents are most beneficial are the places where sourcing the data is harder to find and more costly and time consuming to collect: SME knowledge transfer. Companies around the country have SMEs that are simply overwhelmed by their workflow, don’t have the time for knowledge transfer and require tools to take the repetitive tasks off of their plate so they can be more productive. This is where AI Agents can come in: - They shine where data is scarce and costly to collect - They can handle repetitive tasks, freeing up SMEs for high-value work - They can facilitate knowledge transfer without overburdening experts The challenge? - Data Limitations: SME knowledge is irreplaceable and, as a result, not available online. It’s imperative that devs involve SMEs in the development process while prioritizing the SME’s schedule. Why?  - Calibration is Crucial: AI Agents that do not align with the SME will return irrelevant and useless results wasting time and money. How? - Human-in-the-Loop techniques: Direct SME involvement in calibration yields the best results. Their input fine-tunes AI responses and decision-making. AND - Iterative Improvement: Calibration must be ongoing. Regular feedback loops between AI and SMEs drive continuous enhancement. What does this do? - The AI solution will be built as complement, not replacement, to Human ability:. The goal is to augment SME capabilities, not replace them. This synergy boosts productivity and knowledge sharing. While initial calibration takes time, the long-term gains in efficiency and knowledge dissemination are well worth it. AI Agents can be built responsibly while including our SMEs and improving performance. It takes true engineering and ingenuity to allow AI to move us forward and not replace invaluable talent. 

Stop worshipping prompts. Start engineering the CONTEXT. If the LLM sounds smart but generates nonsense, that’s not really “hallucination” anymore… That’s due to the incomplete context one feeds it, which is (most of the time) unstructured, stale, or missing the things that mattered. But we need to understand that context isn't just the icing anymore, it's the whole damn CAKE that makes or breaks modern AI apps. We’re seeing a shift where initially RAG gave models a library card, and now context engineering principles teach them what to pull, when to pull, and how to best use it without polluting context windows. The most effective systems today are modular, with retrieval, memory, and tool use working together seamlessly. What a modern context-engineered system looks like: • Working memory: the last few turns and interim tool results needed right now. • Long-term memory: user preferences, prior outcomes, and facts stored in vector stores, referenced when useful. • Dynamic retrieval: query rewriting, reranking, and compression before anything hits the context window. • Tools as first-class citizens: APIs, search, MCP servers, etc., invoked when necessary. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: In an AI coding agent, working memory stores the latest compiler errors and recent changes, while long-term memory stores project dependencies and indexed files. The tools fetch API documentation and run web searches when knowledge falls short. The result is faster, more accurate code without hallucinations. So, if you’re building smart Agents today, do this: • Start with optimizing retrieval quality: query rewriting, rerankers, and context compression before the LLM sees anything. • Separate memories: working (short-term) vs. long-term, write back only distilled facts (not entire transcripts) to the long-term memory. • Treat tools like sensors: call them when evidence is missing. Never assume the model just “knows” everything. • Make the context contract explicit: schemas for tools/outputs and lightweight, enforceable system rules. The good news is that your existing RAG stack isn’t obsolete with the emergence of these new principles - it is the foundation. The difference now is orchestration: curating the smallest, sharpest slice of context the model needs to fulfill its job… no more, no less. So, if the model’s output is off, don’t just rewrite the prompt. Review and fix that context, and then watch the model act like it finally understands the assignment!

I used this guide to build 10+ AI Agents Here're my 10 actionable items: 1. Turn your agent into a note-taking machine → Dump plans, decisions, and results into state objects outside the context window → Use scratchpad files or runtime state that persists during sessions → Stop cramming everything into messages - treat state like external storage 2. Be ridiculously picky about what gets into context → Use embeddings to grab only memories that matter for current tasks → Keep simple rules files (like CLAUDE md) that always load → Filter tool descriptions with RAG so agents aren't confused by irrelevant tools 3. Build a memory system that remembers useful stuff → Create semantic, episodic, and procedural memory buckets for facts, experiences, instructions → Use knowledge graphs when embeddings fail for relationship-based retrieval → Avoid ChatGPT's mistake of pulling random location data into unrelated requests 4. Compress like your context window costs $1000 per token → Set auto-summarization at 95% context capacity with no exceptions → Trim old messages with simple heuristics: keep recent, dump middle → Post-process heavy tool outputs immediately - search results don't live forever 5. Split your agent into specialized mini-agents → Give each sub-agent one job and its own isolated context window → Hand off context with quick summaries, not full message histories → Run sub-agents in parallel when possible for isolated exploration 6. Sandbox the heavy stuff away from your LLM → Execute code in environments that isolate objects from context → Store images, files, complex data outside the context window → Only pull summary info back - full objects stay in sandbox 7. Make summarization smart, not just chronological → Train models specifically for agent context compression → Preserve critical decision points while compressing routine chatter → Use different strategies for conversations vs tool outputs 8. Prune context like you're editing a novel → Implement trained pruners that understand relevance, not just recency → Filter based on task relevance while maintaining conversational flow → Adjust pruning aggressiveness based on task complexity 9. Monitor token usage like a hawk → Track exactly where tokens burn in your agent pipeline → Set real-time alerts when context utilization hits dangerous levels → Build dashboards correlating context management with success rates 10. Test everything or admit you're just guessing → A/B test different context strategies and measure performance differences → Create evaluation frameworks testing before/after context engineering changes → Set up continuous feedback loops auto-adjusting context parameters Last but not the least, be open to new ideas and keep learning Check out 50+ AI Agent Tutorials on my profile 👋 .

Day 47 of testing AI tools so you don't have to I've reviewed dozens of AI agent frameworks this year. The pattern is clear: Agents don't fail because the models are weak. They fail because teams skip the boring stuff. Here are the 8 most ignored foundations killing AI Agents in production 👇 1. Tiered Memory Architecture Without separating short-term context from long-term knowledge — and pruning stale data — signal quality degrades fast. Your agent needs: ↳ Cache memory (immediate context) ↳ Episodic memory (recent interactions) ↳ Long-term memory (validated knowledge) Flat memory = hallucination factory. 2. Treating Data as Fuel, Not Logic Agents don't just consume data. They reason through it. Bad data warps decisions. Data quality should be your first priority — not your last. 3. Fallback Mechanisms Without robust fallbacks, agents fail silently instead of degrading safely or escalating to humans. Cascading failures happen when tools, data, or reasoning paths break. Design for graceful failure. Not silent collapse. 4. Problem First, Technology Later Agent sprawl happens when teams chase shiny tech instead of outcomes. No centralized routing, no guardrails, no policies = compliance and reputational risk. Start with the problem. Then build the agent. 5. Knowledge Graphs for Context Vector search alone misses relationships, causality, and constraints. ↳ Vector DBs help agents find information ↳ Knowledge Graphs help agents make accurate decisions You need both for structured reasoning. 6. Policy Enforcement In-Flight Governance must constrain decisions while the agent thinks. Not just validate outputs afterward. This is how you build agents that stay governed at runtime. 7. UX for Asynchronous Autonomy Chat-first UX hides agent progress. Users need iteration trails and proactive status updates. If they can't see what's happening, agents feel broken. 8. Observability and Success Metrics Track what matters: ↳ Task completion rate ↳ Reasoning consistency ↳ Hallucination rate ↳ Tool success rate You can't improve what you can't observe. — These aren't edge cases. They're the difference between demo-ready and production-ready. Which foundation do you see teams skip most often? 👇 ♻️ Share to save someone's agent deployment 💾 Save for your next build

Most teams don’t fail at building AI agents. They fail when those agents collide with real processes, real incentives, and real accountability. This roadmap gets something right that most AI discussions miss: Scaling AI agents isn’t a prompt problem. It’s an operating model problem. Before tools or frameworks matter, a few things must exist. This is the actual sequence that holds up inside enterprises: The components that determine whether AI agents scale or stall: → Pick an LLM Model choice matters, but only after you know what decisions the agent is expected to make and what it’s allowed to touch. → Write system instructions Clear rules, formats, and boundaries. Reusability beats cleverness every time. → Define agent logic How the agent reasons, pauses, and hands off work. Complexity too early creates fragility later. → Add memory (short and long term) Context improves performance. Uncontrolled memory creates risk. Design this deliberately. → Connect tools and APIs Access to the real world must be constrained, observable, and reversible. → Assign a specific job One outcome. One owner. “Be helpful” is not a role. → Build multi-agent systems only when coordination is required Most failures here are handoff failures, not intelligence failures. → Add monitoring and feedback If you can’t see errors, latency, and tool failures, you don’t have reliability. You have luck. → Test, version, and optimize Prompts, logic, and behaviors are software. Treat them that way or expect drift. → Deploy and scale Production is about containment, rollback, and cost control. Demos are irrelevant at this stage. A few truths that tend to separate progress from noise: • Agents fail more often from unclear ownership than weak reasoning • Scale breaks at interfaces, not at models • Adding agents increases complexity faster than capability if design is sloppy • Most “AI issues” are actually process issues wearing new clothes At enterprise scale, an AI agent is not a feature. It’s a digital worker. And digital workers need the same things humans do: – clear responsibility – boundaries – feedback – performance signals – accountability when something breaks If your agents look impressive in demos but struggle in production, the gap is rarely technical. It’s structural. 📌 Save if AI agents are moving onto your enterprise roadmap 🔁 Repost to help teams move beyond prototype theater 👤 Follow Gabriel Millien for execution-level insight on Enterprise AI and transformation Image credit: Greg Coquillo

Stop blaming the model for your agent’s failure. I’ve spent the last few months building AI agents, and I’ve noticed a recurring pattern. When an agent underperforms, people usually try three things: They switch models (GPT-4 to Claude 3.5, etc.) They add more tools. They write 1,000-word prompts. 90% of the time, none of those are the solution. The problem isn't the tech; it's the onboarding. We are treating AI agents like magic boxes instead of new hires. If you want an agent that actually delivers, you have to stop prompting and start designing. Here is the 3-part framework for high-performing agents: 1. The Role (Identity) Giving an agent a persona isn't just "flavor text"—it defines the boundaries of its decision-making. Weak: "You are an assistant." Strong: "You are a Senior Data Analyst with a bias toward statistical significance and skepticism of outliers." Why: Identity drives behavior. It tells the agent what to own and, more importantly, what to escalate. 2. The Goal (Intentionality) "Be helpful" is the fastest way to get a generic response. High-performing agents need a finish line. ❌ "Improve retention." ✅ "Identify the top 3 churn drivers in the last 30 days and propose 2 experiments to reduce them by 5%." Why: Specificity turns a conversationalist into an optimizer. 3. The Backstory (Contextual Reasoning) This is the most underrated step. Backstory provides the "mental model." The Difference: Without backstory, an agent reacts. With backstory, it reasons. The Secret: Encode the "hidden" knowledge. What has it "learned" from past failures? What industry norms should it prioritize under pressure? The Human Test 🧠 If you wouldn’t hire a human teammate without a job description, success metrics, and company context... why do you expect an AI agent to work without them? Great agents aren't just prompted. They are engineered by design. #AIAgents #GenerativeAI #LLMs #FutureOfWork #AIEngineering

There’s a shift away from continuous prompt improvement that’s been observed from Agentic system-based designers. They realize that prompting is the smallest part of the system. What actually drives reliable agent behavior is Context. Not one type of context, but a layered stack that shapes how the agent reasons, decides, and adapts inside real workflows. Here are the seven context types that matter most when you are building production grade agents 👇 1. Memory Context Short term and long term recall that lets the agent use prior messages, past decisions, and persistent knowledge rather than starting from zero each time. 2. Role Context A clear definition of who the agent is supposed to be. Persona, expertise, tone, and responsibility boundaries all anchor consistent behavior. 3. Objective Context The underlying purpose behind the task. When the agent understands intent and success criteria, it can prioritize with far less guesswork. 4. Instructions Context The detailed operating rules. This includes procedures, constraints, and execution steps that shape how the agent performs a task. 5. Examples Context Concrete demonstrations of correct and incorrect outputs. These examples guide the agent toward higher precision and reduce ambiguity. 6. Tools and Results Context Knowledge of which tools are available, how to call them, and how to interpret tool outputs so each new decision uses the most recent information. 7. External Context Everything in the environment that affects the task. Business rules, datasets, APIs, policies, and infrastructure realities that keep the agent grounded. Agents do not become reliable through prompting technique alone. They become reliable when their reasoning is supported by the right context stack. This is the difference between a helpful demo and a system you can trust in production. If you want more technical breakdowns like this on building agentic systems, follow along. And don’t forget to share your experience with the community at large. #AIAgents

When you're deploying AI agents for a CX function, having a good Knowledge Base is a non-negotiable. Why? When optimized, it can empower your AI agents to deliver fast, accurate responses. When neglected, it can leave customers frustrated and agents underperforming. If you want to make sure your help center actually HELPS, here are 5 strategies you can deploy: 1. Structure your content in a Q&A format with clear headings and concise instructions to make it easy for both customers and AI to find relevant information. 2. Use precise keywords. If you have membership tiers, explicitly say which tier you're talking about. 3. Update content regularly with release dates for new features and remove outdated articles. 4. Use visuals (carefully). Reference images and annotations can improve usability—just make sure you have the bandwidth to keep them accurate. 5. Make agents accessible by providing a clear link to the AI agent channels for when customers need help beyond the answers available to them. A lot of companies view help centers as a nice-to-have but the truth is, the ROI is massive. And if you're thinking of using (or already use) AI agents for your customer support, you need to keep it well maintained so the agents can: → Identify knowledge gaps → Make suggestions to make your documentation easier to understand When your help center is optimized, AI agents can perform at their best, which translates to happier customers and less workload for your team. Read the full article for more strategies we recommend—link in the comments! 👇