https://subscription.packtpub.com/video/data/9781806675555/p1/video1_3/developing-enterprise-grade-cognitive-agents-mcp-amp-a2s # **Developing Enterprise-Grade Cognitive Agents (MCP & A2S)** In this session, Imran Ahmad will unpack the architecture and application of autonomous agents by introducing the Modern Cognitive Pipeline (MCP) and Agent-to-System (A2S) interaction models. He will examine how cognitive loops, memory layers, and planning systems empower agents to operate independently and collaborate as specialized teams. Through real-world use cases across healthcare, software engineering, industrial maintenance, and government, attendees will explore multi-agent coordination patterns, decision autonomy frameworks, and deployment strategies. Participants will gain actionable insights into designing, orchestrating, and scaling cognitive agents that integrate safely within enterprise systems, enabling more adaptive, goal-driven automation at scale. This session, **“Developing Enterprise-Grade Cognitive Agents,”** explains what autonomous agents really are, where the hype diverges from reality, and how to assemble cognitive loops, protocols, and frameworks into robust enterprise systems.paste.txt​ --- ## 1. Goals, agenda, and speaker context - The speaker, **Imran**, works at Canada’s Advanced Analytics Solution Center on mission‑critical ML problems, teaches for large platforms, and is writing a book titled **“30 Agents Every AI Engineer Should Know.”**paste.txt​ - Session goals: - Define **autonomous agents** and levels of autonomy. - Contrast **hype vs reality** for agentic systems. - Introduce **cognitive pipelines**, **agent-system models**, and **protocols** (MCP, A2A) for tool and agent communication.paste.txt​ --- ## 2. What are autonomous agents and why now? ## From ML workflows to agents - Traditional production ML is orchestrated via **static workflows**: data ingress, processing, model inference, and output, with fixed steps and no “wisdom”—if something breaks at night, they log or alert but cannot adapt.paste.txt​ - Modern agents add **goal-directed behavior and tool use**: - Given goals like “optimize bank savings,” “run a model,” or “detect theft at the door,” they discover and invoke tools (web search, databases, APIs) based on situational awareness.paste.txt​ ## Definition and levels of autonomy - **Autonomy** = ability to operate independently, make decisions, and adapt to changing circumstances without human intervention.paste.txt​ - The talk defines four levels across a spectrum: - **Level 0 – Workflow**: no autonomy; pure fixed orchestration.paste.txt​ - **Human in the loop**: system proposes decisions, human must approve (e.g., mortgage approvals; agent outputs but human clicks “Approve/Reject”).paste.txt​ - **Human on the loop**: system runs mostly on its own (e.g., autopilot, self‑driving modes) but a human oversees and can intervene.paste.txt​ - **Fully autonomous**: agents operate on their own, with no real‑time human oversight, analogous (conceptually) to human‑level situational awareness, though this level is not yet achievable in practice.paste.txt​ --- ## 3. The cognitive loop: perceive, interpret, decide, act, learn ## Continuous operational cycle - An **autonomous agent** is characterized by a continuous **cognitive loop**: 1. **Perceive** – Gather new data from the environment via sensors/inputs (e.g., door camera, telemetry).paste.txt​ 2. **Interpret** – Process and contextualize raw data into meaningful information at the right “zoom level.”paste.txt​ 3. **Reason & Decide** – Use interpreted info plus internal knowledge to select the best action.paste.txt​ 4. **Act** – Execute actions (call tools, APIs, actuators, etc.).paste.txt​ 5. **Learn** – Update internal state and policies via feedback (reinforcement learning style rewards/penalties) to reduce false positives/negatives over time.paste.txt​ ## Examples and dangers - Example: **Self‑driving car** constantly perceives surroundings, interprets traffic context, plans maneuvers, acts, and adapts from outcomes.paste.txt​ - Critical caution: naive reasoning in high‑stakes settings (e.g., shutting down airplane engines when temperature crosses a threshold) shows why agents must contextualize goals (don’t reduce engine temperature by turning engines off mid‑Atlantic).paste.txt​ --- ## 4. Hype vs reality for cognitive agents ## Common hype - **“Agents will replace developers.”** - Tools like Copilot/Codex accessing repos make this plausible in demos, but full replacement is unrealistic today.paste.txt​ - **“Give it a goal and everything is done.”** - Belief that agents can autonomously achieve any complex objective without human oversight.paste.txt​ - **“One agent can do everything.”** - Conflates wide intelligence with universal competence, assuming a single agent can handle all tasks.paste.txt​ - **“Fully autonomous apps are ready today.”** - Suggests we can already run end‑to‑end enterprise workflows with no humans in/on the loop.paste.txt​ ## Reality and key challenges - **Agents need orchestration and protocols**: - Especially in complex systems, agents must coordinate via frameworks and **shared goals**, embodying a collective philosophy similar to **Ubuntu** (“we rise and fall together”).paste.txt​ - **Tools must be scoped and described carefully**: - Agents’ ability to use tools depends heavily on **tool descriptions**; poorly described or over‑broad tools cause misuse and brittle behavior (classic React‑agent limitation).paste.txt​ - **Memory is brittle and shallow**: - Current agents struggle with persistent, consistent memory; long‑term retention and recall remain open research problems.paste.txt​ - **Hallucinations persist**: - Agents can still fabricate facts, which is unacceptable in critical decision paths; this fuzziness must be managed.paste.txt​ - **Prompt sensitivity**: - Small wording changes in prompts can lead to drastically different behaviors due to embeddings and contextualization, making robust prompt and policy design crucial.paste.txt​ - **State inconsistency**: - When internal memory does not reflect real‑world state (due to glitches or stale info), agent behavior can diverge from expectations.paste.txt​ - **Regulatory and privacy concerns**: - Legal, ethical, and compliance considerations are significant, particularly for critical or user‑facing decisions.paste.txt​ --- ## 5. Evolution of agentic technology - Early AI systems were **rule‑based** (if‑then rules), then moved through **SVMs**, **SPMs**, **deep learning**, and now **LLM‑driven agents**.paste.txt​ - The defining shift: moving from narrow, single‑task systems to **goal‑driven entities** that can leverage **tools, memory, and planning** for wider sets of tasks.paste.txt​ - However, **goal‑seeking behavior** can become problematic when agents pick unsafe or unintended paths to achieve goals, reinforcing the need for oversight and constraints.paste.txt​ --- ## 6. Protocols and architectures: tools and inter‑agent communication ## MCP: agent–tool communication - **MCP (Model Context Protocol)** is introduced as a **universal interface layer** between agents and external tools/data sources.paste.txt​ - Architecture components: - **Servers**: expose data or services (e.g., Slack, databases, document stores).paste.txt​ - **Clients**: live inside or alongside the LLM/agent, connecting to servers.paste.txt​ - **Host**: environment that runs clients and brokers communication.paste.txt​ - MCP’s value: - Replaces ad‑hoc, proprietary integrations with a standardized protocol, reducing **N×M** hardwired connections down to **N+M** and simplifying tool access.paste.txt​ - Focused on **agent ↔ tool**, not agent ↔ agent communication.paste.txt​ ## Agent-to-agent communication (A2A) - Autonomous agents also need **internal and external communication**: - Internally between components like **planning**, **knowledge**, **reasoning**, and **evaluation** modules.paste.txt​ - Externally between distinct agents collaborating toward a shared goal.paste.txt​ - The speaker mentions Google’s emerging **A2A protocol**: - Conceptually, **A2A clients** and **A2A servers** allow agents to talk to each other over standardized channels.paste.txt​ - Implementations are early, but the vision is many agents and servers communicating reliably toward common objectives.paste.txt​ --- ## 7. Agent-system interaction models ## Internal architecture of a cognitive agent - An **agent-system interaction model** breaks an agent into interacting components:paste.txt​ - **Planning**: strategizes future actions and incorporates feedback from past actions. - **Knowledge**: stores learned information, past experiences, and contextual facts. - **Reasoning**: performs logical inference and decision-making over current context and knowledge. - **Evaluation**: assesses outcomes and feeds signals back into planning and learning.paste.txt​ ## Frameworks and when to use them - **LangChain**: - General **LLM application framework**, not just for agents, with strong support for building simple agents (especially **ReAct** agents: **Reason + Act** loop).paste.txt​ - Good fit when building single-agent or modestly complex LLM apps with tool use.paste.txt​ - **CrewAI (Crew)**: - Designed for **multi-agent systems** where multiple specialized agents collaborate toward a shared goal.paste.txt​ - Fits scenarios like the **door‑camera security team** example: - One agent processes video. - Another analyzes packages. - Another can call the police. - Another monitors audio, all working as a **specialized crew** to keep the home safe.paste.txt​ --- ## 8. Enterprise use and risk posture ## Where to place agents today - For **critical processes**, the recommended stance: - Use **human in the loop** as a starting point and **human on the loop** for more mature scenarios.paste.txt​ - Do **not** fully trust agents with safety‑ or finance‑critical decisions (e.g., flight control, autonomous bank transfers) without deterministic safeguards.paste.txt​ - Example risks: - Flight control agent shutting off engines to solve a temperature issue.paste.txt​ - Banking agent mis‑handling multi‑card transactions (e.g., split bill across multiple payment methods) due to unanticipated complexity.paste.txt​ ## Mitigating hallucinations and uncertainty - For critical sub-tasks (e.g., math, financial calculations), rely on **deterministic tools** rather than raw LLM output: - Ask the LLM to **generate code** that is executed, instead of trusting its arithmetic directly.paste.txt​ - Increase robustness by: - Using **multiple specialized agents** instead of one monolithic agent. - Adding **feedback loops** and human oversight. - Quantifying **confidence** only where model behavior is well-understood and bounded.paste.txt​ --- ## 9. Q&A highlights and practical guidance ## Can we use agents for critical processes? - Not yet as fully autonomous controllers; best practice is **human in/on the loop** for high-stakes workflows.paste.txt​ ## How to inject human reasoning and experience? - Use **continuous learning loops**, especially **reinforcement learning**: - Log agent reasoning and decisions, review failures, and adjust policies and rewards. - Combine automated signals (logs, metrics) with human feedback to shape behavior toward human-like reasoning standards.paste.txt​ ## Can hallucinations be eliminated with better certainty estimation? - Hallucination probability can be reduced but not driven to zero; certainty estimates help, but: - Use **specialized agents** and **deterministic components** in critical parts. - Avoid letting a single agent do everything; specialization plus orchestration improves reliability.paste.txt​ ## Choosing base models for agents - For open models: **Code LLaMA** is suggested for in‑house code-capable LLMs.paste.txt​ - For managed cloud: look at **Google’s Model Garden / Vertex AI**, with vertical‑specialized models such as **MedLM/Bio-LM-like models** (e.g., “Bolt”-style medical agents) for domain-specific agent brains.paste.txt​ --- ## 10. Overall message - Building **enterprise-grade cognitive agents** is less about flashy demos and more about: - Designing robust **cognitive loops** with clear perception–interpretation–decision–action–learning stages.paste.txt​ - Using **protocols** like MCP and emerging A2A for tool and agent communication.paste.txt​ - Adopting **multi-agent, specialized teams** orchestrated under shared goals. - Maintaining **human oversight** and deterministic safeguards in critical paths.paste.txt​ 1. [https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/139614499/c9fd2284-b316-4126-b35b-873091643fe9/paste.txt](https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/139614499/c9fd2284-b316-4126-b35b-873091643fe9/paste.txt) 2. [https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/139614499/2ee025f7-cabe-4041-992e-39e39dd2d69e/paste.txt](https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/139614499/2ee025f7-cabe-4041-992e-39e39dd2d69e/paste.txt) ---